Session Keynote-2

Keynote 2

Conference
9:00 AM — 10:30 AM CST
Local
Aug 10 Mon, 9:00 PM — 10:30 PM EDT

Mobile Edge Joint Exploration of Big IoT Data for Smart City Applications

Jenq-Neng Hwang (University of Washington, US)

0
Thanks to the ultra-reliable low-latency communication (URLLC) capability of the emergent 5G mobile networks, the information derived from the roadside static surveillance or on-board moving IoT sensors (e.g., video cameras, Radars and Lidars), which can be jointly explored by the mobile edge computing (MEC) and real-time shared by all the local connected users for various smart city applications. To achieve this goal of coordinated mining of different modalities of IoT data, all of the detected/segmented and tracked human/vehicle objects need to be 3D localized in the world coordinate for effective 3D understanding of local dynamic evolutions. In this talk I will mainly talk about some challenges and potential solutions, more specifically, a robust tracking and 3D localization of detected objects, from either static/moving monocular video cameras, is proposed based on a variant of the Cascade R-CNN detector trained with triplet loss to obtain the accurate localization and the corresponding discriminating identity-aware features for tracking association, even with long-term occlusion, of each detected object in one-shot. When the cameras fail to reliably achieve these tasks due to poor lighting or adverse weather conditions, Radars and Lidars can offer more robust localization than the monocular cameras. However, the semantic information provided by the radio or point cloud data is limited and dif?cult to extract. In this talk, I will also introduce a radio object detection network (RODNet) to detect objects purely from radio signals captured by Radar based on an innovative cross-modal supervision framework, which utilizes the rich information extracted from the camera to teach object detection for Radar without tedious and laborious human labelling of ground truth on the Radar signals. Moreover, to compensate the disadvantage of Lidar detection on far-away small objects, effective integration of Lidar based detections, along with 2D object detections and 3D localization from monocular images based on 3D tracking associations, to achieve superior tracking and 3D localization performance. Finally, an efficient 3D human pose estimation for action description of detected human in natural monocular videos is also presented for finer-grained 3D scene understanding for smart city applications.

5G Evolution and Beyond

Zhenfei Tang (Huawei Technologies Co., Ltd, China)

0
The 5G has already been commercially deployed for more than one year. 3GPP Release 16 was completed in July 2020, which is a major release for 5G because it brought IMT-2020 submission ¨C for an initial full 3GPP 5G system ¨C to its completion. 3GPP 5G is evolving to next releases and is expected to open the door of digital transformation of many aspects of our life, industry, business, and even the whole society ¨C the future of wireless of 5G evolution and beyond is yet to be discovered. Since the first generation of mobile technology, the mobile industry has experienced significant growth driven by ¡®subscription dividend¡¯ and ¡®traffic dividend¡¯. The next dividend is believed to be the ¡°connection dividend¡± or even ¡°intelligence dividend¡±. Not only the number of connected devices and objects will increasing rapidly, but also new applications and business opportunities will greatly emerge. More spectrum, such as new mid-band spectrum at 6GHz, FDD spectrum re-farming and even high frequency spectrum, will be available. 5G evolution and beyond will address those new use cases with advanced wireless technologies. The research challenges and technology breakthroughs required to deliver the vision for future wireless will be presented in this talk.

Session Chair

Tony Quek

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Session CT-02

Information Freshness and Codes

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Closed-form Analysis of Age of Information in Energy Harvesting Network

Siyu Wang (Sun Yat-sen University, China); Xin Wang (Sun Yet-sen University, China); Tianyi Peng, Jiaxi Zhou, Qi Qin and Xijun Wang (Sun Yat-sen University, China)

0
We consider a system in which status updates are generated by a source node at will and are sent to a destination through a wireless channel. The energy consumption of transmission is linearly proportional to service time, which we modeled to be exponentially distributed. Due to the capacity limit of the battery and the vacancy of energy arrivals before the battery runs out, some of the status updates might possibly fail. We analyzed the Age of Information(AoI) for such a system, and show the efficacy of the system parameters on the AoI.

Age-Optimal UAV Trajectory Planning for Information Gathering with Energy Constraints

Xiangjin Zeng, Feipeng Ma, Tingwei Chen and Xuanzhang Chen (School of Electronics and Communication Engineering, Sun Yat-sen University, China); Xijun Wang (Sun Yat-sen University, China)

0
Many time-critical networks based on real-time information gathering by unmanned aerial vehicles (UAV) from a set of ground terminals. For those networks, minimization of the age of information (AoI), a metric proposed recently to measure the freshness of information, is of great importance. In this paper, we consider the problem of minimizing peak age, a metric of AoI, of a network composed of a mobile agent, a charging station and ground terminals. The agent's mobility is constrained by a graph G and energy constraints including battery capacity and charging rate. Aiming at this problem, we study Markov Process and solve it by dimensional reduction. We work out the theoretical minimum peak age under the constraints and propose the least-charging-timed Metropolis-Hastings trajectory, a semi-randomized trajectory proved to be theoretical optimal. Furthermore, we a heuristic trajectory named least-visit-time-based trajectory for the case that visit times for each ground terminal are available for the agent.

Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning

Ruichen Jiang and Sheng Zhou (Tsinghua University, China)

0
In this paper, we study a federated learning system at the wireless edge that uses over-the-air computation (Air-Comp). In such a system, users transmit their messages over a multi-access channel concurrently to achieve fast model aggregation. Recently, an AirComp scheme based on digital modulation has been proposed featuring one-bit gradient quantization and truncated channel inversion at users and a majority-voting based decoder at the fusion center (FC). We propose an improved digital AirComp scheme to relax its requirements on the transmitters, where the users perform phase correction and transmit with full power. To characterize the decoding failure probability at the FC, we introduce the normalized detection signal-to-noise ratio (SNR), which can be interpreted as the effective participation rate of the users. To mitigate wireless fading, we further propose a cluster-based system and design the relay selection scheme based on the normalized detection SNR. By local data fusion within each cluster and relay selection, our scheme can fully exploit the spatial diversity to increase the effective number of participating users and accelerate the model convergence.

Optimizing Information Freshness in Two-Way Relay Networks

Bohai Li (The University of Sydney, Australia); He Chen (The Chinese University of Hong Kong, Hong Kong); Nikolaos Pappas (Linkoping University, Sweden); Yonghui Li (University of Sydney, Australia)

0
In this paper, we investigate an amplify-and-forward (AF) based two-way cooperative status update system, where two sources aim to exchange status updates with each other as timely as possible with the help of a relay. Specifically, the relay receives the sum signal from the two sources in one time slot, and then amplifies and forwards the received signal to both the sources in the next time slot. We adopt a recently proposed concept, the age of information (AoI), to characterize the timeliness of the status updates. Assuming that the two sources are able to generate status updates at the beginning of each time slot (i.e., generate-at-will model), we derive a closed-form expression of the expected weighted sum AoI of the considered system. We further minimize the expected weighted sum AoI by optimizing the transmission power at each node under the peak power constraints. Simulation results corroborate the correctness of our theoretical analysis.

UEP Online Fountain Codes with Sequential Window Strategy

Yifan Duan and Lianghui Ding (Shanghai Jiao Tong University, China); Feng Yang (Shanghai Jiaotong University, China); Liang Qian and Cheng Zhi (Shanghai Jiao Tong University, China)

0
Online fountain code with Unequal Error Protection (UEP) has been proposed to provide better protection for the More Important Symbols (MISs) than that of the Less Important Symbols (LISs). However, the greater priority for MISs is accomplished substantially through choosing MISs with higher probability than that of LISs in encoding. Compared with normal online fountain code, this requires more code symbols to recover all symbols, which leads to high overhead. To reduce the overhead without affecting UEP performance, we propose a novel UEP online fountain codes scheme with sequential window strategy to provide best protection for LISs after decoding MISs in the completion phase. We have theoretically analyzed the upper bound on the overhead of the proposed UEP scheme and evaluated the performance by simulation. Both results show that the proposed scheme has lower overhead than another UEP online fountain codes scheme.

Session Chair

Xijun Wang, He Chen

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Session IoT-04

Resource Allocation

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Dynamic Task Offloading and Resource Allocation for Heterogeneous MEC-enable IoT

Shichao Xia, Wen Xingxing and Yao Zhixiu (Chongqing University of Posts and Telecommunications, China); Yun Li (ChongQing University of Posts and Telecommunications of China, China)

1
With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), the diverse IoT applications with requirements of ultra-low latency and heterogeneous connectivity have been increasing sharply in recent years. In this work, we consider the problem of diverse task offloading and computation resource allocation in the dynamic and heterogeneous MEC-enable network, wherein heterogeneous characterized by the MEC servers with different computing capacities and multi-type applications with different computation requirements. Firstly, to drive the long-term system-wide effectiveness towards a near-optimum, joint task offloading and computation resource allocation is proposed by invoking the Lyapunov optimization theory, and we analyze the tradeoff between task offloading revenue and latency. Besides, a heuristic task offloading methodology based on a search tree is developed to get the optimal offloading strategy in a heterogeneous environment. Moreover, to improve processing efficiency and reduce unnecessary communication overhead, the Offloading Priority Selection Criterion (OPSC) is designed. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulations.

Hypergraph Based Resource Allocation and Interference Management for Multi-Platoon in Vehicular Networks

Hewen Cui (BUPT, China); Lianming Xu, Qing Wei and Li Wang (Beijing University of Posts and Telecommunications, China)

1
Platoon communications in vehicular networks is considered to have broad application prospects in improving road capacity and traffic safety. This paper considers a multi-platoon vehicles scenario, in which platoon vehicles (PVs) transmit cooperative awareness messages (CAMs) to other PVs in the same platoon. The leader vehicles of platoons (L-PVs) use dedicated resource blocks (RBs) to communicate with base station (BS) and broadcast their CAMs to their member vehicles (M-PVs). Meanwhile, to increase spectral efficiency and satisfy the communication demands of PVs, we consider M-PVs reuse RBs with each other to transmit their CAMs. Finally, due to conventional graph theory can only model pairwise relations, we propose a Hypergraph-based Resource Allocation and Interference Management (HRAIM) for multi-platoon scheme to maximize spectral efficiency. Simulation results illustrate that, the proposed scheme shows a better performance in terms of spectral efficiency and sum data rate when compared with traditional graph coloring algorithm and random selection scheme.

A Prediction-Based Spectrum Allocation Scheme for Two-Layer Cellular Vehicular Networks

Qian Li (Northeastern University, China); Weijing Qi and Lei Guo (Chongqing University of Posts and Telecommunications, China)

0
As the number of vehicles and applications in vehicular networks increase, an efficient spectrum allocation method plays an important role in improving resource utilization ratio and relieving network congestion. In this paper, we propose a prediction-based spectrum allocation scheme for two-layer cellular vehicular networks, in order to balance resource satisfaction of users in the whole network. Specifically, considering that the number of vehicles in an area is predictable, we introduce a convolutional long short-term memory (Conv_LSTM) network model to predict the number of vehicles within the range of a small base station (SBS). In addition, since the resource demand of the SBS is related to the number of vehicles, we allocate spectrum resources for SBSs based on the prediction result. The spectrum allocation problem is transformed into a multi-coloring (MC) problem and solved by our proposed spectrum allocation algorithm. Numerical results show that our scheme has high accuracy in predicting the number of vehicles and availability in balancing resource satisfaction.

Mobility Improves the Performance of Collaborated Spectrum Sensing

Huijun Xing (Beihang University, China); Zheng Dezhi and Wang Shuai (Beihang Unicersity, China)

0
Spectrum sharing is a promising technology to solve the problem of the shortage and low utilization of spectrum resources in the future mobile communication systems. Spectrum sensing, as a critical step to discover the available spectrum holes in spectrum sharing, attracts wide attention in academia and industry. The collaborative spectrum sensing allocates sensing tasks to secondary users (SUs), such that the spectrum opportunity of SUs can be guaranteed. However, the impact of mobility on the collaborative spectrum sensing is still unknown. In this paper, we study the impact of mobility on the performance of collaborative spectrum sensing. The multi-user diversity introduced by mobility is applied in the collaborative spectrum sensing, and the performance of spectrum sensing is improved. The detection time and spectrum opportunity of SUs are derived with closed form. We discover that when exploiting the mobility of SUs, the detection time of SUs is reduced and the spectrum opportunity of SUs is improved. Thus the performance of spectrum sensing and spectrum sharing can be improved. This paper provide fundamental guidelines for the design of spectrum sensing mechanisms in the mobile environment.

Multi-user Cooperative Spectrum Sensing Based on the Mean Value of Cumulative Power

Yufei Dai (Beijing Institute of Technology & School of Information and Electronics, China); Liang Liu (China Mobile Communications Corporation & China Mobile Research Institute, China); Dongfang Hu (Beijing Institute of Technology & School of Information and Electronics, China); Han Yang (School of Information and Electronics Beijing Institute of Technology, China)

0
In the practical application of cognitive radio systems, spectrum sensing requires accurate signal detection without any prior knowledge and in complex channel background. Energy detection algorithms perform poorly in complex channel environments, and single-user spectrum sensing is susceptible to problems such as multipath effects, shadow fading, and hidden terminals. This paper analyzes the spectrum sensing algorithm based on the average value of the cumulative power spectral density, and compares it with the traditional energy detection algorithm. In view of the limitations of single-user spectrum sensing, this paper applies multi-user cooperative spectrum sensing to the algorithm, which improves the performance of spectrum sensing.

Session Chair

Qing Wei, Hanlin Mou

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Session MWN-04

UAV

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Invited Talk: Coordinated Transmission on the Ocean for Cell-Free Satellite-UAV-Terrestrial Networks

Wei Feng (Tsinghua University, China)

3
Current maritime coverage mainly relies on onshore base stations (BSs) and marine satellites. The former could only cover limited offshore areas and the later usually lack of broadband communications capability. In this paper, we investigate a new usage of unmanned aerial vehicles (UAVs) under the hybrid satellite-UAV-terrestrial network architecture for better coverage on the ocean. Particularly, we adopt tethered UAVs coordinating with terrestrial BSs along the coastline, so as to liberate UAVs from harsh environments above the ocean and also provide additional aerial BS sites for facilitating the onshore-BS-sites scarcity problem. All spectrum is shared among satellites, UAVs and terrestrial BSs for elastic resource allocation and agile coverage. This hybrid network is irregular due to geographical limitations of terrestrial BSs and the deployment restriction of UAVs, thus leading to more challenging co-channel interference (CCI) for spectrum sharing than conventional cellular networks. We formulate a cell-free coordinated transmission regime, where multiple terrestrial BSs and UAVs form a virtual cluster to jointly serve a ship in a user-centric manner. Inter-cluster interference and leakage interference to satellite users are further mitigated by joint power allocation. The presented method requires only large-scale channel state information (CSI) and practically affordable computing cost, while offering a significant performance improvement in terms of system sum rate.

A Mobility Aware Clustering Scheme Based on Swarm Intelligence in FANETs

Wang Min (Shanghai Jiao Tong University, China)

0
The emerging Flying ad-hoc Network (FANET) has been used in many applications and services ranging from military to emergency rescue. UAV clustering is a significant technology in FANETs. In each cluster, the cluster head (CH) is responsible for the entire cluster management. However, due to the frequent movement and limited energy of UAVs, one of the foremost challenges in FANETs is unstable cluster. Hence, to cope with this issue, we proposed a mobility aware clustering scheme based on swarm intelligence (MACSI) for FANETs, which is enlightened by the animal collective behavior. More specifically, the modified glowworm swarm optimization (GSO) algorithm based on chaos strategy is put forward for CH selection and cluster formation. It aims to help UAVs follow the CH to perform missions by improving global search capability. In addition, a stable CH selection mechanism is adopted, which depends on the residual energy, current position and the movement coordination relationship among neighbors. Our proposal has been evaluated with respect to metrics as energy consumption and cluster life time. The experimental results obtained validate that, the proposed scheme has less energy consumption and extended cluster life time compared with existing clustering schemes.

Joint Access and Backhaul Link Optimization in Multiple UAV-Assisted Emergency Network

Xiaoxu Yuan (Beijing University of Posts and Telecommunications, China); Hui Tian (Beijng university of posts and telecommunications, China); Gaofeng Nie (Beijing University of Posts and Telecommunications, China)

0
With the development of unmanned aerial vehicles (UAVs) in both hardware technology and theoretical approaches, UAVs begin to emerge as important potential equipment for future communication networks, where UAV-assisted emergency communication attracts the most attention due to the flexibility and mobility of UAVs. In this paper, we consider a remote emergency scenario with the ground base stations (BSs) being destroyed and study the access and backhaul link optimization problem. We propose a joint user association, UAV positioning, and resource allocation (UPR) algorithm to maximize the system throughput. We deploy UAVs not only as BSs to provide access links but also as relays to provide backhaul links, where the transmission rate of the system depends on the minimum of all the transmission links. We theoretically analyze the maximization problem of the system throughput and derive the optimal resource allocation for all the transmission links. We also prove the optimality of the equidistant alignment of UAV relays and their linear alignment with the emergency vehicle and solve the resulting optimization problem by alternating between the positioning of UAVs and user association. Simulation results verify the effectiveness of the proposed UPR algorithm.

Flying LTE for UAV Dynamic Access Control

Xiafei Bu and Chungang Yang (Xidian University, China)

0
Unmanned areial vehicles (UAVs) are widely applied to the military and civil domains. These applications are largely depend on UAV communications. UAVs can act as aerial base stations (BSs), aerial relays (ARs) and aerial user equipments (UEs) in networks. However, both high-speed movement of UAVs and dynamic topology make existing medium access control (MAC) protocols not suitable, such as IEEE 802.11. The existing UAV MAC protocols are mostly based on the time division multiple access (TDMA), which studied the improvement of network throughput and delay. These works rarely considered the problem that UAVs are powered by batteries and have limited energy. Thus the orthogonal frequency-division multiplex access (OFDMA) technology is utilized by UAVs to improve the network energy efficiency (EE). This paper first introduces three communication scenarios of UAVs. Then, paper describes several existing UAV MAC protocols and challenges. Next, we propose that UAV BSs access to long term evolution (LTE) networks by OFDMA technology for limited energy of UAVs. Finally, two types of subcarrier quantity allocation are simulated, which one is equal number and the other is based on signal to interference-plus-noise ratio (SINR) between users. The results show that the subcarrier allocation based on the SINR ratio between users has higher throughput. Besides, this allocation is more suitable for dense scenes and has higher EE.

Sum Rate Maximization for UAV-Enabled Wireless Powered NOMA Systems

Jin Du, Zhengqiang Wang, Zifu Fan and Xiaoyu Wan (Chongqing University of Posts and Telecommunications, China)

0
In this paper, we consider the sum rate maximization problem of uplink non-orthogonal multiple access (NOMA) system with wireless power transfer (WPT). The sum rate problem in non-convex problem, which is difficult to handle directly because the energy harvesting time and position of UAV are coupled in the objective function. We propose an iterative algorithm to find the energy harvesting time and the position of UAV. The energy harvesting time problem is solved by the bisection search. Then, the position optimization problem for UAV is solved by quadratic transform method. Finally, based on the above two methods, we propose a two-tier iterative algorithm to jointly optimizing the energy harvesting time and position of UAV. Simulation results show that the proposed algorithm has a better performance compared with the benchmark algorithms.

Session Chair

Chungang Yang, Gaofeng Nie

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Session MWN-07

Code Design and Beamforming

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Beam Management for Cellular-Connected UAVs: A Fast Link Recovery Approach

Jinli Wu (Xidian University, China); Xinhong Mao (Institute of Telecommunication Satellite, China); Ronghui Hou, Xixiang Lv and Hui Li (Xidian University, China)

0
Due to the short wavelength of millimeter wave (mmWave) and high directional beamforming, the massive MIMO systems are highly vulnerable to link blockage. Beam switching to unblocked direction is an effective solution to overcome blockage and restore communication links. To this end, a set of candidate beams for beam switching should be selected before the beam is blocked. However, due to the high speed movement of the UAV, identifying the appropriate beam for an UAV with any position is not trivial. In this work, a fast link recovery approach is proposed. Specifically, our proposed beam selection method considers the spatial correlation, estimated reliability probability of the beams and signal quality. The simulation results show that the proposed method can efficiently recover the interrupted link, and the outage probability is almost reduced to 0% in the scene where the UAV moves at high speed.

Improved method of deblocking filter based on convolutional neural network in VVC

Jing Yang (CQUPT, China); Biao Du and Tong Tang (Chongqing University of Posts and Telecommunications, China)

0
With the rapid development of 5G and the Internet of Things, video has gained broad application space as a carrier of information, and video codec technology can reduce video storage space, save transmission bandwidth, and provide the possibility for the promotion of video applications. Multi-functional video coding (VVC), the latest generation of video coding standard today, still uses block-based coding, which inevitably introduces coding artifacts, and deblocking filters can effectively reduce artifacts and improve the quality of compressed video. The deblocking filter in VVC still adopts the method based on empirical threshold when making the filtering decision, but the actual video scene changes variously, and it is obviously impossible to obtain the optimal filtering effect with a fixed threshold. The decision of the filtering mode is essentially a classification problem. Existing research shows that the convolutional neural network(CNN) has a strong ability in classification tasks, and its feature extraction and nonlinear fitting ability can greatly improve the learning ability of the target, so as to obtain better classification accuracy. Therefore, we improves the deblocking filter in VVC based on CNN. Experimental results show that, compared with the original filtering method of VVC, our method can better improve the quality of compressed video.

Receiver Assisted LBT Mechanism Design for Beam-based Transmission in Unlicensed Bands

Wenxin Wang, Ming Zeng and Fei Zesong (Beijing Institute of Technology, China)

0
In the unlicensed spectrum of 5GHz, Listen-Before-Talk (LBT) technology is used to ensure the fair and friendly coexistence of NR-U (New Radio Unlicensed) and WiFi. However, when signals are transmitted in the mmWave band, the propagation distance is short. Thus, the beamforming technology is used to form a directional narrow beam with high antenna gain, which changes the interference layout, resulting in the limited range of LBT. In this case, to better evaluate the potential interference, the receiver utilizes the useful information received through beam transmission to better manage the interference in the unlicensed mmWave spectrum, which is called receiver-assisted LBT. The performance of the system based on receiver-assisted LBT will be significantly affected by the Energy Detection (ED) threshold of the receiver. Therefore, this paper aims to find the optimal ED threshold of the receiver. We propose the inner approximation algorithm, which can improve the spectral efficiency and the system throughput when multi Radio Access Technologies (RATs) work in the unlicensed mmWave spectrum. The simulation results show the effectiveness of the method.

An Almost Blank Subframe Allocation Algorithm for 5G New Radio in Unlicensed Bands

Qingqing Tang, Ming Zeng, Jing Guo and Fei Zesong (Beijing Institute of Technology, China)

0
The 5G new radio communications in unlicensed bands (5G NR-U) can meet the growing data traffic requirements and make better use of the unlicensed bands. However, the harmonic coexistence of NR-U and WiFi in unlicensed bands is a challenging problem as NR-U and WiFi employ different access technologies. To address this problem, this paper proposes an almost blank subframe (ABS) optimization mechanism by jointly considering the data transmission of WiFi users and the positions of ABS, which can achieve fair coexistence of NR-U and WiFi and effectively improve the system throughput. Specifically, we first investigate the optimal number of ABS according the data transmission of WiFi users. Then, we use the Q-learning algorithm to learn the data transmission rules of WiFi users to solve the problem of matching data transmission positions of WiFi users with ABS positions. Simulation results show that the algorithm can effectively improve the total throughput compared with the traditional ABS allocation algorithm.

Research and Evaluation on Beam Scheduling Algorithm based on Hybrid Beamforming in Millimeter Wave

Hui Liu (China Academy of Information and Communications Technology, China); Jiamo Jiang (China Academy of Information and Communications Technology (CAICT), China); Xia Shen (China Academy of Information and Communication Technology, China); Jiahui Li (Beijing University of Posts and Telecommunications, China)

0
Ministry of Industry and Information Technology of China has issued commercial license for 5G medium and low frequency bands last year, and 5G is in a critical period of commercial deployment. According to national frequency plan, research and development of millimeter wave equipment and technology will be carried out in the next step to prepare for the commercial use of 5G millimeter wave technology. Three schemes to schedule analog beam based on hybrid beamforming in millimeter wave are presented in this article, along with performance evaluation in system-level simulation.

Session Chair

Ronghui Hou, Jing Guo

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Session NGNI-04

Deep Reinforcement Learning and Future Generation Networking

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

A Distributed Reinforcement Learning Approach to In-network Congestion Control

Tianle Mai and Haipeng Yao (Beijing University of Posts and Telecommunications, China); Xing Zhang (BUPT, China); Zehui Xiong and Dusit Niyato (Nanyang Technological University, Singapore)

0
Due to network traffic volatility, congestion control has been a challenging problem faced by network operators. The current network is often over-provisioned to accommodate the worst-case congestion conditions (e.g. links running at only around 30% capacity). Effectively congestion control schemes can enhance the network utilization and lower const. However, for millisecond microburst traffic, it is difficult to be detected and responded timely by traffic engineering or end-host based solutions, which use the feedback signal from the network (e.g., ECNs, RTTs) to adjust the transmission rates. In this paper, we present an in-network scheme, where the congestion control algorithm is directly implemented inside the network to quickly adapt to the volatility. Besides, to enhance the network-scale cooperative control among distributed switches, we introduce a multi-agent deep deterministic policy gradient algorithm, which adopts the centralized learning and distributed execution framework. The extensive simulations are performed on Omnet++ to evaluate our proposed algorithm in comparison to state-of-the-art schemes.

Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

Chen Chen, Zheng Wang and Qingqi Pei (Xidian University, China)

0
In this paper, we first take the moving vehicles as a resource pool (RP), by which we proposed a distributed computation offloading scheme to fully utilize the available resources. After that, we divide a complex task into many small tasks and prove that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. Next, the genetic algorithm is introduced to solve this task assignment problem. Then, for a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our model can make full use of the available computing resources of surrounding vehicles by considering the mobility of the vehicle, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

Towards Mitigating Straggler with Deep Reinforcement Learning in Parameter Server

Haodong Lu (Nanjing University of Posts and Telecommunications, China); Kun Wang (University of California, China)

0
Parameter server paradigm has shown great performance superiority for handling deep learning (DL) applications. One crucial issue in this regard is the presence of stragglers, which significantly retards DL training progress. Previous approaches for solving straggler may not consider the resource utilization of a cluster. This motivates us to make an attempt at designing a new scheme that mitigates straggler problem in DL from the perspective of dynamic balance workloads among workers. To optimize the method of mitigating straggler problem in the traditional parameter server, we propose Help-Control Synchronization (HCS) mechanism which has high flexibility to adapt to the dynamic cluster without parameter settings. Furthermore, we propose a Deep Reinforcement Learning (DRL)- based algorithm Parallel Actor-critic-based Experience Replay (PAER) that can automatically identify and determine helper workers (helper) and helpee workers (helpee). The whole idea has been implemented in a scheme called FlexHS which mitigates straggler problem by creating a dynamic balance between the number of helper and backup overhead. Evaluation under various algorithms evidences the superiority of our scheme.

Deep Reinforcement Learning Based task scheduling in edge computing networks

Qi Fan and Zhuo Li (Beijing Information Science and Technology University, China); Xin Chen (Beijing Information Science & Technology University, China)

0
Existing cloud computing services are widely used, but there are large delays and bandwidth requirements. Edge computing has become the hope of reducing service delay and traffic in 5G networks. However, the performance of end-user task offloading in edge computing scenarios depends on the efficient management of various network resources. Therefore, the coordinated deployment of computing and communication becomes the biggest challenge. This paper solves the problems of offloading strategies and edge resource allocation of computing tasks, and proposes a joint optimization solution for edge computing scenarios. Because wireless signals and service requests have random properties, we use an actor-critic-based deep reinforcement learning framework to solve optimization problems to minimize the average end-to-end delay. Since the state and action space in the problem is very large, a deep neural network (DNN) is used as a function approximator to evaluate the value function in the critic section. Participants partly used another DNN to represent the parameterized random strategy and improved the strategy with the help of reviewers. In addition, a natural strategy gradient method is used to avoid converging to a local maximum. In the simulation experiment, we analyzed the performance of the algorithm and proved that the algorithm has a clear advantage in reducing latency costs over other schemes.

AoI-driven Fresh Situation Awareness by UAV Swarm: Collaborative DRL-based Energy-Efficient Trajectory Control and Data Processing

Wen Fan and Ke Luo (Sun Yat-sen University, China); Shuai Yu (Sun Yat-Sen University, China); Zhi Zhou and Xu Chen (Sun Yat-sen University, China)

0
In many delay-sensitive monitoring and surveillance applications, unmanned aerial vehicles (UAVs) can act as edge servers in the air to coordinate with base stations (BSs) for in-suit data collection and processing in order to achieve real-time situation awareness. In order to ensure the long-term freshness requirements of situation awareness, a swarm of UAVs need to fly frequently among different sensing regions. However, non-stop flying and in-suit data processing may quickly drain the batteries armed in UAVs, hence an energy-efficient algorithm for UAVs' dynamic trajectory planning as well as proper data offloading is highly desirable. To better model the problem, we propose a freshness function based on the concept of Age-of-Information to express the freshness of situation awareness. We adopt a novel multi-agent deep reinforcement learning (DRL) algorithm with global-local rewards to solve the established continuous online decision-making problem involving many UAVs for achieving efficient collaborative control. Extensive simulation results show that our proposed algorithm can achieve the most efficient performance compared to six other baselines, i.e., our algorithm is able to significantly reduce the energy consumption while keeping the global situation awareness at a very fresh level.

Session Chair

Huawei Huang, Yaqiong Liu

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Session SPC-04

Localization

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

An Enhanced Indoor Localization System Using Crowdsourced Multi-Source Measurements

Biheng Yang, Bin Li, Lyuxiao Yang and Nan Wu (Beijing Institute of Technology, China)

0
With the rapid development of the mobile Internet, applications based on indoor localization have received increasing attention. In recent years, WiFi received signal strength (RSS) is widely used in indoor localization for the universally available WiFi infrastructure. However, the WiFi signal could easily be affected by non-line-of-sight (NLOS) and multipath propagation, which reduces the localization accuracy. In this paper, we propose an enhanced indoor localization system using multi-source measurements including WiFi RSS, ultra wideband (UWB) ranging, and inertial sensors to improve the performance. The multi-source measurements collected by the users' smartphones are used for site survey in our system. To recover the users' trajectories, we propose a crowdsourcing method to construct the radio map. Moreover, a reference point clustering approach is used to improve the system efficiency. A two-step localization method is proposed to locate the user. Experimental results show that the proposed system achieves better performance than either only WiFi-based or UWB-based method.

DOA Estimation for Arbitrarily Distributed Subarrays in UAV Swarm

Dian Fan (The China Academy of Information and Communications Technology); Gan Guo, Jiaming Song and Lanfei Li (The China Academy of Information and Communications Technology, China); Yue Zhu (Beijing Jiaotong University, China)

0
In this paper, we consider the problem of DOA estimation for the arbitrarily distributed subarrays with application in unmanned aerial vehicle (UAV) swarm, where multiple small UAVs equipped with uniform linear array (ULA) are separated by unknown intervals due to the dynamic moving. Three parameters are used to formulate the steering vector, i.e., the direction of arrivals (DOAs) of the target users, the intervals of UAVs, and the orientation angles of UAVs. We first estimate the orientation angles using an auxiliary user and then obtain the DOAs via a search free rooting method, regardless of the intervals of the UAVs. The deterministic Cram\'er-Rao bound (CRB) of the DOA and orientation angle are derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

Positioning with Dual Reconfigurable Intelligent Surfaces in Millimeter-Wave MIMO Systems

Jingwen Zhang, Zhong Zheng and Fei Zesong (Beijing Institute of Technology, China); Xuyan Bao (Beijing University of Posts and Telecommunications, China)

0
The combination of reconfigurable intelligent surface (RIS) and millimeter-wave (mm-wave) multiple-input multiple-output (MIMO) has drawn much attention for future wireless networks. In this paper, the RISs are used to assist the base station (BS) to localize the user equipment (UE) in the mm-wave MIMO systems, where the reference signals sent by the UE are measured by the BS and the information of the direct UE-BS path and the reflection paths via RISs are utilized in the localization. In particular, the delay difference between the direct path and reflection path as well as the angles of each path are used to construct the triangulation needed in positioning. Therein, a two-stage positioning method with dual RISs is proposed, where in the first stage one suitable RIS is chosen to reflect signals and in the second stage location information is estimated. In particular, the optimal phase shifts of the RIS elements are obtained and the optimal RIS selection criterion is derived. Numerical results show that the proposed method achieves a localization accuracy up to 10 -5 to 10 -4 meter.

Three-Dimensional Localization of RF Emitters: A Semantic Segmentation-based Image Processing Approach

Huichao Chen, Zheng Wang and Wei Wang (Nanjing University of Aeronautics and Astronautics, China); Guoru Ding (Army Engineering University of PLA & Southeast University, China)

1
Localization is an important issue in wireless sensor networks (WSNs). Aimed at the shortcomings of low localization accuracy of the existing 3D localization algorithms, in this paper, we develop a three-dimensional localization scheme of RF emitters which combines collaborative spectrum sensing with deep learning. We propose a semantic segmentation approach to identify the coverage range of the RF emitters which converts the three-dimensional sensing data into a series of two-dimensional image slices. Then, we design a weighted localization algorithm to accurately locate the RF emitters. The simulation results show that the proposed method is accurate in positioning under various parameter configurations.

CSI-based Indoor Localization Error Bound Considering Pedestrian Motion

Zhenya Zhang, Liang Bo Xie, Mu Zhou and Yong Wang (Chongqing University of Posts and Telecommunications, China)

0
Compared with the Wi-Fi Received Signal Strength (RSS), which is commonly used for indoor pedestrian motion detection and localization, the Channel State Information (CSI) can be used to achieve higher localization accuracy since it contains the finer-grained physical-layer information. Due to the lack of theoretical analysis towards the indoor localization error bound based on the CSI considering the pedestrian motion, it is difficult to evaluate the performance of the existing indoor localization methods when pedestrians are moving. In this case, this paper proposes the Cramer-Rao Lower Bound (CRLB) concept to derive out the indoor localization error bound by leveraging the pedestrian motion that depends on the constructed signal propagation model and the environment and device factors and also analyzes the impact of the asynchronous effect on the error bound. The comprehensive simulation validates the proposed theoretical analysis.

Session Chair

Dian Fan, Feifei Gao

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Session WCS-07

Advanced Wireless Techniques I

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Blocking- and Delay-aware Flow Control Using Markov Decision Process

Yixuan Wang (Xi'an University of Posts & Telecommunications, China); Changyin Sun (College of Xi'an Post and Communication, China); Fan Jiang (Xian University of Posts and Telecommunications, China); Jing Jiang (Xi'an University of Posts and Telecommunications, China)

0
In the transition period of 5G, LTE/5G dual-connectivity(DC) is a key solution which can not only meet the user's traffic demand, but also can provide offloading flexibility for hot spot data services. To further enhance the dual connectivity performance, the mmWave technique is adopted in low power node. Although be promising, the enhanced framework still face challenges due to the limits in practical deployment, such as delay between the interface of different nodes, and intermittent transmission of blocked mmWave link. In this paper, we address the flow control algorithm of the mmWave enhanced dual connectivity scenario, where a user equipment (UE) downloads a file with split bearer architecture of DC, taking into account the link's blocking state, the delay difference of split data flow transmission, energy consumption and QoS of UE. We model this problem as a finite level discrete-time Markov decision process, and use the dynamic programming algorithm to get the optimal solution. The performance of the proposed flow control algorithm is verified by a large number of simulations. The results indicate that even under the uncertainties of blocking, the transmission can be finished energy efficiently without violation of delay budget. Moreover, as synchronization between the different links of split bearer are kept, packet loss due to reordering window is avoided.

Energy Consumption Minimization using Data Compression in Mobile Edge Computing

Bo Wang, Yaqiong Liu, Guochu Shou and Yihong Hu (Beijing University of Posts and Telecommunications, China)

0
In recent years, many researchers have done a lot of work on mobile edge computing (MEC). However, existing works on energy minimization of MEC systems mainly focus on comparing energy consumption between accomplishing tasks locally and computing tasks on MEC server, failing to explore energy optimal solutions for computing tasks on MEC server. In this paper, we consider a single-user-single-server MEC system, working under latency constraint. In order to reduce the energy consumption of mobile device and meet the latency constraint, we use computation offloading to accomplish large tasks. However, energy consumed by mobile devices (MD) for the transmitting process could be decreased further by compressing data transmitted from MD to mobile-edge server (MEC server). But energy consumed when compressing data on MD increases while the compression rate decreases. Therefore, we develop a solution to figure out the optimal compression rate to minimize the total energy for MD to accomplish one simple task and to meet the latency constraint at the same time. With the optimal compression rate, energy consumption for task-process could be reduced by 38.37% to 60.49% when the latency constraint varies from 0.1s to 0.8s.

Multiuser Offloading Strategy Based on User's Computing Ability in Massive MIMO System

Gong Xinyu (Zhengzhou University, China)

0
Due to the rapid development of wireless networks, there is a mass of computation requirements for mobile devices. Many computation tasks can be offloaded from networking devices to edge helpers to extend battery life, make efficient use of idling edge computation resources, and address their limitations. This paper provides a convex optimization policy for multiuser computation offloading in the massive MIMO system, which could realize the lower total energy consumption (include local consumption and transmission consumption) in a coherent time. We take into account the user's local computing ability and the channel transmission capacity in the case of binary offloading and partial offloading. For binary offloading, we consider the possibility that individual users cannot compute locally because of limited computing ability. And we always include these users in the offloading list, then turn other offloaded users into locally computed users to use channel capacity more efficiently. Next, in the condition of partial offloading, we reapportion the input data for individual users according to the average channel capacity or maximum local computing ability. Simulation results have shown that the computing resources can be fully utilized without overload and the total energy consumption would be reduced considerably.

Timing Advance Estimation With Robustness to Frequency Offset in Satellite Mobile Communications

Li Zhen (Xi'an University of Posts and Telecommunications, China); Keping Yu (Waseda University, Japan); Guangyue Lu (Xi'an University of Posts & Telecommunications, China); Yukun Zhang (Xi'an University of Posts and Telecommunications, China)

0
Timing advance (TA) estimation based on Zadoff-Chu (ZC) sequences is susceptible to carrier frequency offset (CFO), especially in high-dynamic satellite mobile communication scenarios with large Doppler shift. To solve this problem, a novel random access (RA) preamble sequence is first constructed by combining the real and imaginary part of a root ZC sequence, which entirely inherits the excellent correlation properties of the ZC sequence. With the aim of mitigating the adverse impact of large CFO, we further present a multi-peak joint detection algorithm that can obtain accurate TA value in once correlation operation without additional resource consumption and computational complexities. Numerical results consist with the mathematical analysis, and exhibit the robustness of the proposed method to large CFO in terms of error detection probability (EDP) and timing mean square error (MSE).

Joint Power Allocation for a Novel Positioning-communication Integrated Signal

Lu Yin, Jiameng Cao, Tianrun Jiang and Zhongliang Deng (Beijing University of Posts and Telecommunications, China)

0
This paper develops a positioning-communication joint power allocation method for a novel positioning-communication integrated signal called Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA). One of the main differences between the MS-NOMA and the traditional positioning signal is MS-NOMA supports configurable powers for different positioning users (P-Users) to obtain better ranging accuracy and signal coverage. In this paper, the proposed joint power allocation method minimizes the average range measurement error of all P-Users in the network. Meanwhile, it guarantees QoS (Quality of Services) requirements and total transmit power budget of all users, including P-Users and communication users (C-Users). The numerical results show the effectiveness of the proposed joint power allocation method.

Session Chair

Fan Jiang, Keping Yu

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Session WCS-10

Advanced Wireless Techniques IV

Conference
10:30 AM — 12:00 PM CST
Local
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Comparison of OFDM and SC-FDE for VLC Systems with a Nonlinear LED Model

Hao Zhang (Shandong University, China); Jian Sun (ShanDong University, China); Wensheng Zhang and Zhiquan Bai (Shandong University, China); Cheng-Xiang Wang (Southeast University & Heriot-Watt University, China)

2
Single-carrier frequency domain equalization (SC-FDE) and orthogonal frequency division multiplexing (OFDM) are two attractive technologies for visible light communications (VLC). In this paper, we analyze the two schemes in VLC system with direct current (DC) biasing. We investigate the bit error rate (BER) performance of SC-FDE and OFDM schemes with a memory nonlinear light-emitting diode (LED) model in a simulated indoor scenario. The system performance between both schemes and the impact of LED nonlinear distortion are compared and discussed. We through numerical simulations found that the OFDM scheme exhibits a better BER performance compared to SC-FDE scheme although having a higher peak-to-average power ratio (PAPR).

Transmission Diversity Schemes for Downlink Control Channel in 5G

Qin Mu (Xiaomi Inc, China); Yuqiang Chen, Kexin Xiong and Chenxi Liu (Beijing University of Posts and Telecommunications, China)

0
The 5 th generation(5G) cellular network could provide ever best performance to mobile customers, with higher system capacity, massive connections and lower transmission latency. Physical downlink control channel(CCH) is a core element due to its crucial function to the data transmission in the whole system. In particular, the transmission diversity scheme for downlink control channel is one fundamental aspect to influence the coverage and spectral efficiency. Considering the amount of resources to be used for reference signal will be greatly compressed compared with long term evolution (LTE) system, the existing transmission diversity schemes for CCH, such as spatial frequency block codes (SFBC) and resource element (RE)- based random beamforming would suffer severe block error rate (BLER) loss due to their sensitivity to channel estimation error. To improve the BLER performance, this paper proposes the resource element group (REG) bundle-level random beamforming with the adaptive size of the REG bundle to the aggregation levels of CCH. Moreover, corresponding search space design is further proposed to prevent increase of the CCH blocking. The effectiveness of the proposed methods is validated by link level simulation and numerical analysis.

Delay-Aware Energy Minimization Offloading Scheme for Mobile Edge Computing

Fan Jiang (Xian University of Posts and Telecommunications, China); Fengmiao Wei (Xi'an University of Posts and Telecommunications, Xi'an, China); Junxuan Wang (Xi'an University of Post and Telecommunications, China); Xinying Liu (Keysight Technologies (China) CO., LTD, China)

1
Offloading is regarded as a promising technology to reduce the delay and energy consumption of computation application in Mobile Edge Computing (MEC) network. By considering the demand of request user for low energy consumption and dynamic offloading environment, this paper proposes a offloading strategy by achieving the tradeoff between the energy consumption and time delay of computation application. Specifically, the computation offloading decision is first formulated as a finite horizon Markov decision problem. Then, based on dynamic programming method, a Delay-Aware joint (Device-to-Device) D2D, MEC and Local Offloading (DADMLO) algorithm was proposed to get the optimal offloading policy which aims at minimizing the energy consumption of request user before deadline. Simulation results demonstrate that compared with heuristic schemes, the proposed strategy can complete the computation application with a higher completion probability and lower energy consumption.

An Interference Suppression Method Based on Space-Eigen Adaptive Processing for Satellite Communications

Mengyun Zhao (University of Electronic Science and Technology of China, China); Hongzhi Zhao (UESTC, China); Wenbo Guo and Youxi Tang (University of Electronic Science and Technology of China, China)

1
Aiming at the problem that satellite communications are susceptible to be interfered from every directions, we propose an interference suppression method based on space-eigen adaptive processing (SEAP) to improve the anti-interference capability of satellite receivers. By exploiting the focused distribution characteristics of interference power in eigen domain, the received signals are transformed from time domain to eigen domain for interference suppression, and then inversely transformed back to the time domain for subsequent signal processing. Theoretical analysis and numerical simulation results show that the proposed SEAP algorithm retains the advantage of suppressing interferences in different arriving directions, especially has significant gain for co-directional narrowband interference (NBI).

Cost-Oriented Cooperative Caching Scheme in Energy-Harvesting-Powered Ultra-Dense Networks

Jing Song and Peng Lin (Northeastern University, China); Qingyang Song and Lei Guo (Chongqing University of Posts and Telecommunications, China)

0
Caching contents at wireless edge nodes has been regarded as a promising solution for improving quality of experience (QoE) and reducing energy consumption. Integrating caching to energy-harvesting-powered (EH-powered) ultra-dense small cell networks (UD-SCNs) is an efficient way to alleviate the burdens of backhaul links and further save on-grid energy. In this paper, we propose a cooperative caching scheme which can balance two caching actions, caching the most popular contents and caching diverse contents, in EH-powered UD-SCNs. The former helps reducing transmission delay while the latter contributes to saving on-grid energy. We regard delay and energy as two types of cost and introduce a weighted cost function. Then, the caching problem is transformed to be a cost minimization problem, and a genetic algorithm (GA) is designed to solve the problem in an effective way. Numerical results demonstrate that compared with the existing cooperative caching schemes, the proposed cooperative caching scheme succeeds in balancing transmission delay reduction against on-grid energy saving.

Session Chair

Wensheng Zhang, Jie Yang

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Session CT-03

Communication Security

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

High Precision Indoor Positioning Method with Less Fingerprints Collection on 60GHz

Qiuna Niu (College of Information Science & Engineering, Ocean University of China, Qingdao, China); Hongping Li (College of Information Science & Engineering, Ocean University of China, Qingdao, China); Shi Wei (College of Information Science & Technology, Qingdao University of Science & Technology, Qingdao, China)

0
Facing huge demand for high precision indoor location services, an improved fingerprint-based positioning on 60GHz is proposed. Unlike conventional fingerprint methods, the algorithm adopts Time of Arrival (TOA)-based ranging values as location fingerprints due to high time resolution of 60GHz. To reduce the expense of fingerprint collection in the offline phrase, the fingerprints collection of the target location space is separated into two steps. In addition, the Gaussian Progress Regression (GPR) technique is applied to predict the missing ranging measurements. In the online phrase, sparse signal reconstruction algorithm based on Compressive Sensing (CS) is employed twice to achieve coarse and fine positioning. As the simulation results shown, the centimeter positioning accuracy can be obtained.

Achieving one-time pad via endogenous secret keys in wireless communication

Liang Jin (Zhengzhou Information Science and Technology Institution, China); Xu Wang (National Digital Switching System Engineering and Technological R&D Center, China); Yangming Lou (China National Digital Switching System Engineering and Technological R&D Center, China); Xiaoming Xu (National Digital Switching System Engineering & Technological Research Center, China)

1
The open and broadcast nature of wireless channels makes eavesdropping possible, leading to the endogenous problem of information leakage. Endogenous problems should be solved by endogenous security functions. Accordingly, wireless security problems should be resolved by channel-based endogenous security mechanisms. Firstly, this paper analyzes the endogenous security principle of the physical-layer-secret-key method. Afterward, we propose a novel conjecture that in a fast-fading environment, there must exist wireless systems where the endogenous secret key rate can match the user data rate. Moreover, the conjecture is well founded by the instantiation validation in a wireless system with BPSK input from the perspectives of both theoretical analysis and simulation experiments. These results indicate that it is possible to accomplish one-time pad via endogenous secret keys in wireless communication.

Achievable Rate of Multi-Antenna WSRNs with EH Constraint in the presence of a Jammer

Minhan Tian, Wangmei Guo, Guiguo Feng and Jingliang Gao (Xidian University, China)

0
In this paper, the rate-energy region is studied for the wireless sensor relay network (WSRN) with energy harvesting in the presence of a jammer. In the model, a source communicates to a destination equipped with a single antenna with energy harvesting constraint through a multi-antenna cooperative relay under beamforming. Meanwhile, there is a jammer intended to disturb the communication. The relay works in half-duplex mode and knows all the channel state information (CSI). When beamforming is employed at the relay, the network can be modeled as an equivalent Gaussian arbitrarily varying channel (GAVC). We characterize the achievable rate-energy region. Since the problem is non-convex, we present a stability method to transform it into a semi-definite programming problem (SDP), and the closed-form expression for two special boundary points of the rate-energy region is obtained. Finally, the simulations show the rate-energy region and the anti-jamming performance of the proposed scheme.

A Decode-and-Forward Relay-Aided Proactive Eavesdropping Scheme for Wireless Surveillance

Haowei Wu (Chongqing University, China); Lian Yan (School of Microelectronics and Communication Engineering, Chongqing University, China); Rui Ma (Chongqing University & Center of Communication and Tracking Telemetry Command, China); Jinglan Ou (Chongqing University, China); Jingyue Cui (School of Microelectronics and Communication Engineering, Chongqing University, China)

1
Legitimate wireless surveillance plays an important and preventive role in protecting public safety. To promote the eavesdropping performance of wireless surveillance systems, a proactive eavesdropping scheme with a decode-and-forward relay is proposed, where the relay intercepts suspicious information and interferes with the suspicious receiver simultaneously. The closed-form expressions are derived, including the decoding outage probability, eavesdropping outage probability, and average eavesdropping rate. To maximize the average eavesdropping rate, the transmit power and location deployment at the relay are optimized. Specifically, the closed-form result of approximate optimal transmit power for the relay is obtained, and the bisection-based algorithm is further proposed to verify its correctness. Simulation results validate that the proposed scheme achieves better eavesdropping performance than that without optimization.

Secrecy Rate Maximization in Millimeter Wave SWIPT Systems based on Non-Linear Energy Harvesting

Gangcan Sun (Zhengzhou University, China); Mengyuan Ma, Zhengyu Zhu, Jinlei Xu and Wanming Hao (Zhengzhou University, China)

0
In this paper, we study the secrecy rate maximization in millimeter-wave simultaneous wireless information and power transfer systems, where two radio frequency chain antenna architectures are considered. Then, a joint optimization problem of digital precoding vector, power splitting ratio and artificial noise covariance matrix is proposed, while non-linear energy harvesting and maximum transmit power constraint are considered. It is difficult to solve directly due to coupled variables and non-convexity, so we propose an alternating optimization algorithm based on semi-definite relaxation to solve it. Meanwhile, an alternating optimization algorithm based on zero forcing precoding is proposed to reduce the complexity. Finally, simulation results prove the validity of the proposed different algorithms.

Session Chair

Haowei Wu, Qiuna Niu

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Session IoT-05

mMTC

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

A Novel Virtual Small Cell-Based Group-Paging Scheme for Massive MTCs over LTE Networks

Linh T. Hoang and Anh-Tuan Hoang Bui (The University of Aizu, Japan); Chuyen T. Nguyen (Hanoi University of Science and Technology, Vietnam); Anh T. Pham (The University of Aizu, Japan)

1
Group paging (GP) is one of the improvements proposed by 3GPP on the radio access network (RAN) of LTE/LTE-A to enable machine-type communications (MTCs). Nevertheless, the conventional GP might still pose an overload on RAN when a massive number of paged MTC devices trigger the LTE's contention-based random access procedure (RAP) in a highly synchronized manner. This paper, based on the concept of Virtual Small Cells (VSCs), introduces a new RAP for MTC devices to solve the RAN overload issue in the group-paging process caused by the massive access. The proposed VSC-based RAP is designed based on the group-based access manner and an adaptive access barring algorithm, which efficiently controls the access rate in each VSC during the paging process. Computer simulation shows that, in the context of massive MTCs, the proposed VSC-based scheme can significantly outperform the conventional GP in terms of success access rate and average delay of successfully-accessed devices.

Preamble Split Transmission and Joint Active User Detection for Massive Connectivity

Lin jie Yang, Pingzhi Fan, Li Li and Li Hao (Southwest Jiaotong University, China)

0
Low-power delay-tolerable services are very important applications of future large-scale IoT. Active user detection is its critical challenge. By exploiting the sparsity of the received signal, this challenge could be converted to a compressive sensing problem and hence solved by the approximated message passing (AMP) algorithm. In order to improve the active user detection performance, a preamble split transmission (PST) random access (RA) scheme is proposed, in which each partition of the preamble is sent in different coherent time duration to achieve the time diversity. Correspondingly, a joint active user detection (JAUD) algorithm is proposed to jointly detect the distributed split preambles at the base station. Simulation results show that the proposed access scheme achieves a higher detection accuracy at the cost of a longer access delay. According to the delay-tolerable characteristic of the target network, this cost could be acceptable.

Maximum Sum Rate of Slotted Aloha for mMTC with Short Packet

Weihua Liu (Sun Yat-sen University, China); Xinghua Sun, Wen Zhan and Xijun Wang (Sun Yat-sen University, China)

0
As one of three generic services to be supported by the fifth-generation (5G), massive machine type communication (mMTC) is envisioned to support short-packet transmissions, which leads to 1) advantage of grant-free access due to a small signalling overhead; 2) loss of information encoding rate in the finite blocklength region. As one of the representative grant-free schemes, slotted Aloha has gained renewed interests in MTC networks recently, yet its optimal performance in the finite blocklength region remains largely unexplored.

Toward the above issue, this paper focuses on optimizing the sum rate performance of a slotted Aloha network with retry limit, where each node encodes k information bits to a packet with the blocklength N, and transmits over an Additive white Gaussian noise (AWGN) channel. The probability of successful transmissions of data packets is derived, based on which the network sum rate is obtained as an explicit function of key system parameters. Further by jointly tuning both the transmission probabilities of nodes and the blocklength of packets, the maximum sum rate is characterized. The analysis reveals the effect of the number of information bits per packet k and the retry limit M on the optimal sum rate performance. It is found that the retry limit M does not affect the maximum sum rate, while a larger number of information bits per packet ameliorates the maximum sum rate in the finite blocklength region.

An Incentive Mechanism for Nondeterministic Vehicular Crowdsensing with Blockchain

Fan Li, Changle Li, Yuchuan Fu and Pincan Zhao (Xidian University, China)

1
With the increase in the number of on-board sensors, vehicles have shown great potential in mobile crowdsensing. To ensure the capability of the vehicular crowdsensing system, it is necessary to inspire sufficient vehicles to participate. However, due to personal interests and privacy protection, this goal is not easy to achieve. In addition, uncertain mobility of vehicles also brings challenges to the design of incentive mechanisms. In this paper, we propose an incentive mechanism for nondeterministic vehicular crowdsensing with blockchain (INVCB), which can effectively incentivize vehicles while protecting user privacy. We first propose a framework for nondeterministic vehicular crowdsensing with blockchain and design a series of smart contracts to automate the crowdsensing process. Then, in order to improve the quality of sensing data, we add reputation attribute to each user, and provide an incentive mechanism that considers reputation. Extensive simulation results show the performance of our proposal is reliable and effective.

User Scheduling for Information Freshness over Correlated Markov Channels

Yanzhi Huang, Xijun Wang, Xinghua Sun and Xiang Chen (Sun Yat-sen University, China)

0
There is a surge of need for fresh information with the overwhelming proliferation of the Internet of Things (IoT) applications. To characterize the information freshness perceived by the destination, age of information (AoI) has been proposed. In this paper, we consider an IoT system with multiple IoT devices sending time-sensitive information to a central controller through time-correlated Markov channels. Our goal is to design a user scheduling policy that minimizes the time-average of AoI under a scheduling constraint. We formulate the AoI minimization problem by the restless multi-armed bandit. With Lagrangian relaxation, we establish the indexability and obtain Whittle's index in closed-form. A scheduling policy is further proposed based on the Whittle's index. Simulation results show that the proposed scheduling policy can achieve comparable performance with the optimal policy.

Session Chair

Neng Ye, Hao Liu

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Session MWN-05

Performance Analysis and Optimization

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

Performance Analysis of the Coexistence of 5G NR-Unlicensed and Wi-Fi with Mode Selection

Yifan Jiang, Jing Guo and Zesong Fei (Beijing Institute of Technology, China)

0
By allowing to utilize the unlicensed spectrum band, the New Radio Unlicensed (NR-U) is a key technology of the fifth-generation (5G) wireless system to relief the spectrum shortage issue of licensed band. In this paper, we investigate the WiFi and 5G NR coexisted network, where the NR base stations implement the mode selection procedure to use the licensed spectrum band or the unlicensed spectrum band. Leveraging the stochastic geometry, we present a tractable mathematical framework to characterize the medium access probability, the conditional coverage probability for different types of access points and the overall coverage probability. The accuracy of the analytical evaluations is validated by simulation. Our results show that the incorporation of NR-U can improve the overall network performance by comparing with the performance of the stand-alone cellular network. In addition, there is an optimal mode selection probability (i.e., the probability of switching to use the unlicensed band) which maximizes the overall coverage probability.

Adaptive Fast Simplified Successive Cancellation List Polar Decoding based on Path Selecting

Ling Wang (Chongqing University of Posts and Telecommunications, China)

0
5G is designed for high reliability and low latency massive data interaction scenarios. Compared with 4G mobile communication, it puts forward higher requirements for channel decoding. To improve the error correction performance and reduce delay, some improved simplified successive-cancellation list (SSCL) have been proposed in the past. The fast SSCL exploits some special constituent nodes and gives the bound of path splitting to reduce the required number of time-steps. In this paper, an adaptive path selecting strategy based on path metric (PM) is proposed, which induces the sorting complexity and tightens the exact bound of path splitting of the fast SSCL. Simulation results show that, the proposed scheme has lower complexity and higher efficiency than SCL without any performance loss. Also, compare to the empirical Fast-SCL, it shows better error correction performance. Moreover, the proposed scheme outperforms existing adaptive decoding algorithms, avoiding any priori information as a threshold stored in memory.

Statistical QoS Provisioning Resource Allocation Over SWIPT Based Relay Networks

Ya Gao (Luoyang Normal University, China); Yongpeng Shi and Yujie Xia (Luoyang Normal College, China); Hailin Zhang (Xidian University, China)

0
Energy scarcity becomes a key factor restricting the development of the wireless communications network. Wireless power transfer, as it can provide a sustainable and reliable energy supply for wireless communications devices, has drawn a lot of interests in recent a few years. Thus, combining wireless power transfer to the wireless communications network can potentially extend the lifetime of wireless devices and thus prolong the operating-time of networks. In this paper, we consider the two-hop wireless networks, where the source node transmits information to the destination assisted by the relay node and the relay simultaneously harvests energy and receive information from the source node. In order to maximize the effective capacity (EC), which is the maximum constant arrival rate under specified quality of service (QoS) requirements, we investigate the statistical QoS supported resource allocation policies under full duplex transmission mode for wireless powered relay networks. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

A Deep Reinforcement Learning-Based Caching Strategy for Internet of Things

Ali Nasehzadeh and Ping Wang (York University, Canada)

1
With the continuous growth of the Internet of Things (IoT), the specific needs of these networks are becoming clearer. Transient data generated and limited energy resources are two of the characteristics of IoT networks that impose some limitations. Moreover, the conventional quality of service requirements such as minimum delay, are still needed in these networks. By implementing an efficient caching policy, it is possible to meet the conventional demands while easing the specific limitations of IoT networks. By leveraging deep reinforcement learning technique, without the need of prior knowledge of the contents popularity, contents life-times or any other type of contextual information, we have managed to develop a caching policy which increases the cache hit rate and decreases the energy consumption of IoT devices while simultaneously considering the limited life time of the data contents. The simulation results show that our proposed method outperforms the conventional Least Recently Used (LRU) method by considerable margins in all aspects.

Maximizing Lifetime of Delay-Tolerant Sensor Networks With a Mobile Sink

Haoliang Li, Peiliang Zuo, Hanbo Jing and Wenbo Wang (Beijing University of Posts and Telecommunications, China)

0
In this paper we investigate the network lifetime maximization problem of the delay-tolerant sensor network where a mobile sink is dispatched to collect aggregated data from the sensors with different available energy levels and diverse storage sizes. The data gathering process is also subject to the bounded delay determined by the traffic and storage size of each sensor. To settle the NP-hard problem, we obtain the data distribution constraint for sensor nodes in degree-constrained data-gathering trees. Based on which the traffic of a node could be approximately estimated by the generated data of its descendant nodes that are within a certain number of hops. We then propose a distributed heuristic method which is composed of three procedures, i.e. selecting residence locations for the mobile sink, constructing a load-balanced data-gathering forest and pruning branches to meet delay requirements. We finally conduct extensive simulations to verify the performance of the proposed method against other heuristics, the simulation results show that the proposed method significantly outperforms them in terms of the network lifetime.

Session Chair

Ya Gao, Ping Wang

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Session MWN-08

Resource Allocation and Optimization

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

A Cooperative Coded Caching Strategy for D2D-Enabled Cellular Networks

Yunpeng Ma (Northeastern University, China); Weijing Qi (Chongqing University of Posts and Telecommunications, China); Peng Lin and Mengru Wu (Northeastern University, China); Lei Guo (Chongqing University of Posts and Telecommunications, China)

0
With the explosive increase of data traffic in wireless networks, caching video contents by device-to-device (D2D) communication has emerged as an effective solution for alleviating the burden of backhaul links. How to improve cooperative caching between mobile users in D2D-enabled cellular networks is crucial. In this paper, we propose a cooperative coded caching (CCC) strategy for D2D-enabled cellular networks to minimize the delivery delay. In our method, users are divided into different social clusters through an interest-dependent Chinese restaurant process (ICRP) since users request different content based on their interests. Moreover, we exploit the maximum distance separable (MDS) code to encode the contents into distinct segments. We propose a caching algorithm based on a greedy approach to obtain the optimal content placement. Numerical results demonstrate the effectiveness of our proposed caching strategy in terms of average delivery delay.

User Preference and Activity Aware Content Sharing in Wireless D2D Caching Networks

Yulong Qi, Jingjing Luo and Lin Gao (Harbin Institute of Technology, Shenzhen, China); Fu-Chun Zheng (Harbin Institute of Technology, Shenzhen, China & University of York, United Kingdom (Great Britain)); Li Yu (Huazhong University of Science & Technology, China)

0
Device-to-Device (D2D) content sharing has emerged as an important tool to alleviate the backhaul pressure. Most of prior works optimize D2D caching policies with known content popularity, which may not be the case in reality. In this paper, we investigate a D2D caching optimization problem with unknown content popularity in wireless D2D caching networks. To maximize the overall D2D caching hit rate, we propose a distributed caching policy by learning user preferences and user activity levels. For the first time, we exploit the sliding time window method to predict real-time user activity levels. And we employ a logistic regression model to describe the user preference. By predicting user activity levels and user preferences in real time, the proposed policy not only can significantly improve the overall D2D caching hit rate, but also reduce the traffic load of the base station compared to existing policies. Simulation results with MovieLens dataset further show that the overall D2D caching hit rate of our proposed policy is close to that of the optimal caching policy.

IoT Gateway Association and Data Scheduling for Delay Optimization in LEO Satellite Systems

Chong Liu and Rong Chai (Chongqing University of Posts and Telecommunications, China); Qianbin Chen (Chongqing University of Posts and Telecommunication, China)

0
As a critical supplementary to terrestrial communication systems, low-earth-orbit (LEO) satellite communication systems have been gaining growing attention in recent years. In this paper, we consider an LEO system where a number of Internet of things (IoT) gateways need to send their collected data to LEO satellites. The highly dynamic topology of satellite systems and the limited connection status between satellites and gateways pose challenges to the problem of gateway association and data scheduling. To address this problem, we define system transmission delay as the number of time slots needed for all gateways to upload their collected data, and formulate the joint gateway association and data scheduling problem as a system transmission delay minimization problem. To solve the problem, we first consider a relatively simple case, i.e., a single gateway case, and propose a greedy method-based single gateway association and data scheduling algorithm. We then extend the solution to the case of multiple gateways and propose a transmission priority-based joint gateway association and data scheduling algorithm. Simulation results show that the proposed algorithm is superior to the algorithm proposed in previous work.

Cooperative mechanism of entity state information search with trajectory prediction

Puning Zhang, Xuefang Li and Xu yuan Kang (Chongqing University of Posts and Telecommunications, China)

0
In recent years, with the great progress made in embedded technology and communication technology, the number of mobile sensing devices such as smart phones, tablets, and wearable devices has grown exponentially. Such products have certain sensing, storing, computing, and communicating capabilities, and are able to complete some simple sensing tasks. Due to the influences of the location, observation period, energy supply and other factors on the fixed-deployed sensing equipment, the state information acquired by the search system in the process of matching the entity state users demand may not be accurate and complete. In view of this situation, acquiring physical state information through mobile smart devices such as smart phones, tablets, and wearable devices is a good solution. Selecting mobile intelligent devices with high sensing quality to work cooperatively to provide search services is vital, which takes advantage of the high mobility characteristic of mobile intelligent devices to fully acquire entity state information. Based on the research on how to realize the search collaboration among mobile intelligent devices, this paper uses the mobile intelligent devices' moving trajectories to predict whether they have the ability to participate in tasks, and then evaluates their sensing quality according to the historical task completion situation, and finally selects the mobile intelligent devices with high sensing quality to complete the search sensing tasks.

Session Chair

Guanglun Huang, Rong Chai

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Session NGNI-05

Future Generation Networking and Computing

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

Adaptive Learning-Based Multi-Vehicle Task Offloading

Hao Qin, Guoping Tan and Siyuan Zhou (Hohai University, China); Yong Ren (Zhongyun Intelligent Network Data Industry, China)

0
In vehicular mobile edge computing, vehicles can provide computing services via V2V communication. Vehicles offload the task to the vehicles which own computing resources at each time period. Most of the available literature focus on the task offloading to a single vehicle. In this work, we propose a multi-vehicle task offloading based on the multi-armed bandit theory to meet the real-time requirements in the dynamic environment. Specifically, we propose a system model for multi-vehicle task offloading with the help of V2V communication. Then, we put forward a multi-vehicle task offloading algorithm based on the adaptive learning. Finally, we perform simulations and the results confirm that the proposed algorithm can effectively reduce the offloading transmission delay and improve the resource utilization than the method of offloading tasks to a single vehicle.

WorkerFirst: Worker-Centric Model Selection for Federated Learning in Mobile Edge Computing

Huawei Huang and Yang Yang (Sun Yat-Sen University, China)

0
Federated Learning (FL) is viewed as a promising manner of distributed machine learning, because it leverages the rich local datasets of various participants while preserving their privacy. Particularly under the fifth-generation communications (5G) networks, FL shows its overwhelming advantages in the context of mobile edge computing (MEC). However, from the participant's viewpoint, a puzzle is how to guarantee the trade-off between the profit brought by participating in FL training and the restriction of its battery capacity. Because communicating with the FL server and training an FL model locally are energy-hungry. To address such a puzzle, different from existing studies, we particularly formulate the model-selection problem from the standpoint of mobile participants (i.e., workers). We then exploit the framework of deep reinforcement learning (DRL) to reformulate a joint optimization for all FL participants, by considering the energy consumption, training timespan, and communication overheads of workers, simultaneously. To address the proposed worker-centric selection problem, we devised a double deep Q-learning Network (DDQN) algorithm and a deep Q-Learning (DQL) algorithm to strive for the adaptive model-selection decisions of each energy-sensitive participant under a varying MEC environment. The simulation results show that the proposed DDQN and DQL algorithms can quickly learn a good policy without knowing any prior knowledge of network conditions, and outperform other baselines.

Network Cost Optimization-based Controller Deployment for SDN

Chunling Du and Qianbin Chen (Chongqing University of Posts and Telecommunications, China); Jinyan Li and Lei Zhang (China Telecom Technology Innovation Center, China)

0
Software-defined networking (SDN) is expected to simplify network management and offer efficient and flexible supports to diverse user services. To meet the demanding transmission requirements of various SDN switches, the optimal deployment of controllers has become an important problem. In this paper, the capacitated controller deployment problem is studied for SDN where a number of candidate controllers with given constrained capacity can be deployed to enable centralized management in the control plane of SDN. Considering both transmission time and cost of the controllers, we formulate the capacitated controller deployment problem as a network cost minimization problem. To solve the optimization problem, we propose a two-stage heuristic algorithm which first tackles the controller deployment problem under the unlimited capacity constraint, and then solves the controller-capacity matching problem. Specifically, in the first stage, we study uncapacitated controller deployment problem and propose a minimum eccentricity-based controller deployment strategy to determine the number and location of controllers. In the second stage, considering the capacity constraint and cost of candidate controllers, we propose an iterative Kuhn-Munkres (K-M) algorithm to solve the controller matching problem. Simulation results verify the effectiveness of the proposed algorithm.

Double Attention-based Deformable Convolutional Network for Recommendation

Honglong Chen, Zhe Li and Kai Lin (China University of Petroleum, China); Vladimir V. Shakhov (University of Ulsan, Korea (South)); Leyi Shi (China University of Petroleum, China)

0
Data sparsity is one of the serious problems in recommender systems, which can be tremendously alleviated by making use of informative reviews and deep learning technologies. In this paper, we propose a Double Attention-based Deformable Convolutional Network (DADCN) for recommendation. In the proposed DADCN, two parallel deformable convolutional networks, which adopt the word-level and review-level attention mechanisms, are designed to flexibly extract the deep semantic features of both users and items from reviews. The combination of two parallel deformable convolutional networks with the word-level and review-level attention mechanisms helps to capture representative user preferences and item attributes. Extensive experimental results on four real-world datasets demonstrate that the proposed DADCN outperforms the baseline methods.

Session Chair

Zhuo Li, Xiao Lin

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Session SPC-05

Coding and Decoding

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

Probabilistic Shaping Combined With Spatially-Coupled LDPC Code in FTN System

Jiali Xie and Sheng Wu (Beijing University of Posts and Telecommunications, China); Yizhen Jia (Academy of Military Sciences PLA China, China)

0
In this paper, we propose a combination of probabilistic shaping and spatially-coupled low-density parity-check code in Faster-Than-Nyquist system. At the transmitter, a constellation signal conforming to a certain probability distribution is generated. After passing through the additive white Gaussian noise channel, the receiver uses the iterative equalization method of stream decoding to decode due to the before-and-after correlation characteristics of the spatially-coupled low-density parity-check codeword. The system proposed in this paper is simulated based on the probabilistic shaping 16QAM format. With the spectral efficiency of 2.67 bit/s/hz, the frame error performance under the Faster-Than-Nyquist system and the Nyquist system is compared. It shows that in the Faster-Than-Nyquist system, the probabilistic shaping and spatial-coupling low-density parity-check code combination have 0.83 dB system gain and 0.53 dB shaping gain when compared to Nyquist system.

A Low Complexity Successive Cancellation List Decoding Algorithm of Polar Codes

Jiansong Miao, Weijie Li, Xuejia Hu and Hairui Li (Beijing University of Posts and Telecommunications, China)

0
Polar codes are the first provably capacity-achieving novel channel codes. Successive cancellation list (SCL) decoding algorithm has a performance close to maximum-likelihood decoding when code length is finite, though the complexity is high. To reduce the complexity of SCL, a partial path expansion with segmented check and pruning SCL (PPE-SCP-SCL) algorithm is proposed in this paper. Based on the successive decoding structure, if the decoding result of a bit is judged to be reliable enough, then a hard decision is made directly and no path expansion is necessary. Moreover, several parity-check points are introduced to perform segmented parity-check during the decoding process, and if the check fails, the current path is directly abandoned. The rationality of the operations are theoretically verified, and numerical simulations also verify that the proposed algorithm can significantly reduce the complexity of decoding with trivial loss of error performance.

A Parallel and Memory-Efficient Decoding for Spatially-Coupled LDPC Codes

Qihao Wu (Beijing University of Posts and Telecommunications, China); Lihong Lv (Beijing Space Information Relay and Transmission Technology Research Center, China); Yanjun Yao (No. 38 Research Institute of China Electronic Technology Corporation, China); Sheng Wu (Beijing University of Posts and Telecommunications, China)

0
In this paper, we propose a parallel and memory-efficient decoding for spatially-coupled low-density parity-check (SC LDPC) codes. The new decoding was obtained by applying parallel architecture and efficient memory management to the windowed decoding. Simulation results show that the new decoding greatly reduces decoding latency and requires less memory, and there is no performance degradation. The advantage of the proposed decoding make it appealing in practical applications, especially in low latency scenario.

Modulation Classification in Successive Relaying Systems with Interference

Tao Li (Xidian University & State Key Laboratory of Integrated Services Networks, China); Wei Liu (The 7th Research Institute of China Electronics Technology Group Corporation, China); Xiaoyu Jiang (CETC Advanced Mobile Communication Innovation Center, China); Yongzhao Li (Xidian University, China)

0
Modulation classification plays an important role in various civil and military applications. In this paper, we study the modulation classification for successive relaying multiple-input multiple-output (MIMO) systems with interference. To mitigate the effect of interference from another relay node, we adopt a method that jointly identify the modulation types of the source node and the relay node. Specifically, higher order moments (HOMs) and higher order cumulants (HOCs) of the received signal are used as the discriminating features in random forest (RF) to classify the modulation types. Extensive simulations verify the advantages of the proposed scheme over the traditional scheme that treats the interference as colored noise.

Enabling Joint Tx-Rx Spatial Modulation with RF Mirrors

Chaowen Liu, Yihua Dong and Liu Boyang (Xi'an University of Posts and Telecommunications, China); Guangyue Lu (Xi'an University of Posts & Telecommunications, China); Pengyu Zhai (Xi'an University of Posts & Telecommunications, China)

0
Joint Tx-Rx spatial modulation (JSM) is a recently emerged multiple-input multiple-output (MIMO) transmission scheme, which is able to simultaneously achieve the transmit diversity, receive diversity and multiplexing gain. In this article, we propose and investigate a novel JSM framework, where the antennas of the transmitter are surrounded by RF mirrors. In contrast to the conventional JSM, the novel JSM is implemented by utilizing a single RF chain powered transmitter, and hence is capable of achieving decreased cost and improved flexibility for realization. To provide our novel JSM with enhanced diversity, an alternative null-space beamforming (ANB) based transmitter preprocessing scheme is proposed. To achieve different tradeoff between reliability and complexity, we introduce two types of detection algorithms, i.e., the maximum-likelihood detection (MLD) and the receiving power sorting based detection. Furthermore, we analyse the capacity and the error performance of the proposed ANB-JSM systems employing the MLD, so as to gain insights into the advantages of the novel JSM. Numerical results demonstrate that our JSM can outperform its conventional counterpart in the aspects of both performance and applicability.

Session Chair

Sheng Wu, Feifei Gao

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Session WCS-08

Advanced Wireless Techniques II

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

On the Node Energy Efficiency of Full Duplex Two-way Ultra-Reliable Short Packet Communications

Zhihao Ye, Zhengchuan Chen, Yunjian Jia and Liang Liang (Chongqing University, China); Min Wang (Chongqing University of Posts and Telecommunications, China)

0
Full-duplex (FD) is a promising technique to improve the spectrum efficiency in wireless communications. However, it is noteworthy that there is a game for both node performances in a two-way channel, since self-interference can not be eliminated completely in FD communications. In this paper, we investigate the node energy efficiency (EE) for FD two-way ultra-reliable short packet communications in an additive white Gaussian noise (AWGN) channel. We focus on two cases. One is that the two nodes are noncooperative, each node only intends to maximize its own EE at any moment. The other one is that two nodes are cooperative, i.e., either of them is willing to improve their optimal EE simultaneously. For the first case, we establish a Game in terms of node EE and design an iterative algorithm to search for a Nash Equilibrium. In the second case, we establish a convex optimization problem of node EE and find the optimal solution by a standard convex optimization algorithm. It is shown by numerical results that the iterative algorithm converges and each node can achieve a higher optimal EE with a lower transmit power if they cooperate with each other.

A Unified Framework for Communications, Computing and Caching Resources Allocation in Mobile Networks

Yingjiao Li, Yaping Sun, Zhiyong Chen, Meixia Tao and Wenjun Zhang (Shanghai Jiao Tong University, China)

0
Mobile services become more and more diversified, that is, there are distinct requirements on the communications, computing and caching (3C) resources in mobile systems. Different from the traditional work that only considers one service type, this paper proposes a unified framework to optimize the 3C resources of base station and mobile devices for diversified services. In the proposed framework, we design a model that the task required by the mobile device to be generated at BS with the mobile edge computing server, the mobile device or both of them to characterize the communication-intensive, computing-intensive and 3C-intensive services. We formulate the optimization problem on the computing and caching resource of the mobile devices and the transmission bandwidth for minimizing the total delay while maximizing the number of the tasks requested and executed by the mobile devices as a multi-objective programming (MOP). Moreover, we propose a single-objective iterative algorithm (SOIA) to this complicated optimization problem, and a quick search algorithm is put forward to obtain the exact Pareto-optimal points in one mobile device scenario. Both algorithms are compared with the traditional multi-objective evolutionary algorithm MOEA/D, and the results show the significant performance in the paper.

PAPR Suppressing Discrete Fourier Transform Precoding-based DSSS-GFDM Transceiver for 5G Satellite Communications

Huanyu Liu, Yuan Jiang and Lin Zhang (Sun Yat-sen University, China)

0
In order to address the issue that the orthogonality could hardly be guaranteed over satellite channels, the generalized frequency division multiplexing (GFDM) has been applied and combined with the direct sequence spread spectrum (DSSS) to combat the carrier frequency offset (CFO). However, due to the superposition of signals over multiple subcarriers, GFDM based systems still suffer from the high peak-to-average power ratio (PAPR). In order to suppress the PAPR while retaining the reliability performances over satellite channels, we propose to utilize the discrete Fourier transform (DFT) precoding to spread the signals in the time domain, thus the probability of superposing signals having the same phase is lowered, thereby leading to the improved PAPR performances. At the receiver, reverse operations are conducted to retrieve the estimates. Furthermore, we analyze the PAPR performances for the proposed design. Then we provide the simulation results to validate that the proposed transceiver could effectively suppress the PAPR performances, while retaining satisfactory BER performances over the additive white Gaussian noise (AWGN) and the international telecommunication union (ITU) satellite channels without or with CFO.

Analysis of UWB Antenna with the MoM Based on RWG-SWG Hybrid Basis Function

Huaijun Zhou and Wei Liu (National University of Defense Technology, China)

0
In view of the insufficient theoretical analysis of UWB antenna by the method of moments (MoM), the detailed process of analyzing UWB antenna by the MoM is presented comprehensively. UWB antenna can be divided into triangle and tetrahedron subdivisions. The surface current distribution is represented by triangle basis function, and the electric displacement vector is represented by tetrahedron basis function. The impedance matrix is divided into four parts: conductor-to-conductor, conductor-to-dielectric, dielectric-to-conductor and dielectric-to-dielectric. The boundary conditions between conductor and dielectric are considered. The formulas for calculating the impedance and current coefficients are obtained. The above algorithm is used to calculate S11 of UWB antenna. The simulated results with FEKO agree well with the results calculated with program, which shows that the algorithm is correct and effective. The realization of this algorithm lays a solid theoretical foundation and provides a good guidance for the optimization and design of UWB antenna.

Particle Swarm Optimization Algorithm based Multi-Path Channel Model Simplification

Mingwei Tang (Beijing University of Posts and Telecommunications, China); Hang Long (Beijing University of Posts & Telecommunications, China); Yixiao Li (Beijing University of Posts and Telecommunications, China)

0
In a system with a large bandwidth, the number of resolvable paths of the channel model is particularly large, making the computer simulation of the channel model and related applications extremely complicated. Therefore, it is necessary to simplify the channel model on the premise of retaining the basic characteristics of the channel. Our goal is to construct a channel model with less paths to approximate the original multi-path channel model, and the absolute error between the frequency correlation functions of the two is used to measure the similarity between them. In this paper, we propose an improved particle swarm optimization algorithm to simplify a multi-path channel. And Lagrangian multiplier method is applied to calculate the power parameters of the simplified channel. Simulation results show that the performance of this algorithm is better than the weighted merger method.

Session Chair

Yunjian Jia, Zhongyuan Zhao

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Session WCS-11

Advanced Wireless Techniques V

Conference
1:30 PM — 3:00 PM CST
Local
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

Extended SCMA Graphs for Block Fading Channels

Shannan Mou, Jincheng Dai and Zhongwei Si (Beijing University of Posts and Telecommunications, China)

0
Sparse code multiple access (SCMA) has been considered as one of the candidate techniques to realize non-orthogonal multiuser transmission. In this paper we investigate the SCMA transmission in block fading channels with arbitrary coherent bandwidth. The extension from the SCMA base graph is performed by the cyclic shift of identity matrices. We derive the condition for generating the extended factor graph to combat the block fading, and then we present an algorithm for constructing the superposition matrix simply. Numerical results in terms of symbol error rate is evaluated, which shows that the proposed structure significantly outperforms the base graph and the randomly extended graph. The full diversity can be achieved in block fading channels by following the proposed algorithm.

A Fast Beam Training Method for 5G New Radio

Lei Wang (Beijing Jiaotong University, China); Bo Ai (Beijing Jiaotong University & State Key Lab of Rail Traffic Control and Safety, China); Yong Niu (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China); Zhangdui Zhong (Beijing Jiaotong University, China); Qi Wang (Huawei Technologies, China); Zhu Han (University of Houston, USA)

0
Millimeter wave communication requires a highly directional transmission link between the base station and the user equipment, which may increase the time of initial access and affect the experience of delay-sensitive users. Considering the initial access technique based on analog beamforming, this paper proposes a fast beam training method for random combination of beam pairs. The method first divides the beam directions of the base station and the user into several groups, and then randomly selects beams from both groups to determine whether it meets the requirement for establishing a link. If so, the algorithm stops the search, otherwise it continues to select beams randomly from the remaining part until a proper beam pair is found. This paper first introduces the beam training process of random combination method, and then compares it with exhaustive method and iterative method in terms of discover delay and misdetection probability. Simulation results show that the random combination method can effectively reduce discover delay and shorten the initial access time.

Recovering Missing Values from Corrupted Historical Spectrum Observations for Dependable Spectrum Prediction

Xi Li, Zhicheng Liu and Yinfei Xu (Southeast University, China); Xin Wang (Stony Brook University, USA); Tiecheng Song (National Mobile Communications Research Laboratory, Southeast University, China)

0
Spectrum prediction is a promising technology to infer spectrum state from historical spectrum observations, by exploiting the inherent correlations and regularities among them. Due to the common existence of missing values and anomalies in the real-world spectrum observations, spectrum prediction with incomplete and corrupted historical observations has caused extensive concern. In this paper, we aim to tackle the challenging problem on how to accurately and efficiently recover the missing values from corrupted historical spectrum observations with which dependable spectrum prediction can be performed. To this end, we first formulate a hankelized time-structured spectrum tensor that can naturally preserve both spectral and temporal dependencies among the historical spectrum observations. Then we model the spectrum data recovery as a tensor completion problem by exploiting its latent low-rank structure and sparse anomaly property. To efficiently solve the optimization problem, we design a robust online spectrum data recovery algorithm based on the alternating direction method. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art schemes and confirm its effectiveness for dependable spectrum prediction.

Outage Performance Analysis of Full-Correlated Rayleigh MIMO Channels

Huan Zhang (University of Macau, Macao); Guanghua Yang and Zheng Shi (Jinan University, China); Shaodan Ma (University of Macau, China); Hong Wang (Nanjing University of Posts and Telecommunications, China)

0
The outage performance of multiple-input multiple-output (MIMO) technique has received intensive attention to meet the stringent requirement of reliable communications for 5G applications, e.g., mission-critical machine-type communication (cMTC). To account for spatial correlation effects at both transmit and receive sides, the full-correlated Rayleigh MIMO fading channels are modeled according to Kronecker correlation structure in this paper. The outage probability is expressed as a weighted sum of the generalized Fox's H functions. The simple analytical result empowers asymptotic outage analysis at high signal-to-noise ratio (SNR), which not only reveal helpful insights into understanding the behavior of fading effects, but also offer useful design guideline for MIMO configurations. Particularly, the negative impact of the spatial correlation on the outage probability is revealed by using the concept of majorization, and the asymptotic outage probability is proved to be a monotonically increasing and convex function of the transmission rate. In the end, the analytical results are validated through extensive numerical experiments.

Session Chair

Yue Xiu, Hong Wang

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Session CIS-04

Physical Layer Security

Conference
3:10 PM — 4:40 PM CST
Local
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

Physical Layer Secret Key Generation Based on Autoencoder for Weakly Correlated Channels

JingYuan Han, Xin Zeng and Xiaoping Xue (Tongji University, China); Jingxiao Ma (Tongji University, United Kingdom (Great Britain))

0
The reciprocity of the wireless channel is the basis of secret key generation on the physical layer as an emerging method for information security enhancement. Due to the duplex mode of communication systems, the channel responses detected by two connected legitimate nodes are not completely reciprocal. In this paper, an efficient physical layer secret key generation scheme is proposed based on an autoencoder to extract reciprocal features from weakly correlated channel estimates. The encoder part after training is equipped at both nodes to generate features for quantization in various channel environments. Simulation results show lower mean squared error (MSE) and better mutual information of the extracted features comparing to the most efficient linear generation method, the principal component analysis (PCA) based one. It reveals great potential to generate symmetric secret keys with better performance particularly in low SNR scenarios.

Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems

Wei Sun (Northeastern University, China); Qingyang Song and Lei Guo (Chongqing University of Posts and Telecommunications, China); Jun Zhao (Nanyang Technological University, Singapore)

0
Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices. Moreover, the combination of the IRS and the SWIPT can create the "one plus one greater than two" effect. However, due to the broadcast nature of wireless media, the IRS-aided SWIPT systems are vulnerable to eavesdropping. In this paper, we study the security issue of the IRS-aided SWIPT systems. The objective is to maximize the secrecy rate by jointly designing the transmit beamforming and artificial noise (AN) covariance matrix at a base station (BS) and reflective beamforming at an IRS, under transmit power constraint at the BS and energy harvesting (EH) constraints at multiple energy receivers. To tackle the formulated non-convex problem, we first employ an alternating optimization (AO) algorithm to decouple the coupling variables. Then, reflective beamforming, transmit beamforming and AN covariance matrix can be optimized by using a penalty-based algorithm and a semidefinite relaxation (SDR) method, respectively. Simulation results demonstrate the effectiveness of the proposed scheme over baseline schemes.

Proactive Eavesdropping Scheme via Decode-and-Forward Relay with Multiple Full-Duplex Antennas

Lisheng Yang (Chongqing University, China); Jingyue Cui (School of Microelectronics and Communication Engineering, Chongqing University, China); Rui Ma (Chongqing University & Center of Communication and Tracking Telemetry Command, China); Haowei Wu and Jinglan Ou (Chongqing University, China)

1
This paper studies a legitimate proactive eavesdropping scenario where a central monitor covertly wiretaps the communications between suspicious users through a full-duplex multi-antenna relay. Specifically, the suspicious information is intercepted by the eavesdropping relay, and then encoded and forwarded to the central monitor. At the same time, the relay sends jamming signals to the suspect destination to reduce the quality of suspicious communication. To minimize the suspicious secrecy rate, the optimal forwarding and jamming beamforming vectors of the eavesdropping relay are first designed. Based on this optimized result, the best power allocation ratio is obtained. To this end, the optimal beamforming vectors and power allocation ratio are derived in closed-form. The simulation results verify the existence of the optimal forwarding power of the relay and show that the proposed scheme achieves better performance than the conventional schemes.

Dual-antenna Time-delay Countermeasure Against Passive Location System

Shiqi Zhang, Pinyi Ren and Qinghe Du (Xi'an Jiaotong University, China)

0
This paper considers countermeasure technology of satellite passive location system based on TDOA measurement. Considering that TDOA measurement is based on the principle of time difference estimation on emitter signal received by different receivers, a technology of the same signal form spread spectrum signals with different time delay emitted by two antennas in emitter is proposed. The center vectors of the two antennas point to different directions, which makes the power ratios of the two antennas signals received by different satellites different. Then, the paper compares the theoretical derivation of the time difference estimation between traditional location method and dual-antenna time-delay technology, and introduces the satellite passive location algorithm and location error index. Finally, the simulation results show that the time difference value estimated by receivers is wrong after using the dual-antenna time-delay technology, and location error in the satellite coverage area increases effectively. The closer the angle of the antenna center vector is to the ground, the better the effect will be. It is of great significance in protecting the communication security of users.

Secure Transmission in FDD MBM Systems Using Analog Feedback

Xiaomeng Zhou, Zhenzhen Gao and Xuewen Liao (Xi'an Jiaotong University, China)

0
In this paper, the physical layer security (PLS) issue is studied for frequency-division duplex (FDD) multiple-input multiple-output (MIMO) media-based modulation (MBM) systems with a multiple-antenna eavesdropper. A more practical scenario is considered that only the statistic channel state information (CSI) of the eavesdropping link is available and the imperfect CSI of the legitimate link is acquired by analog feedback. Based on these imperfect CSI, an artificial-noised-aided precoding scheme is proposed, and an effective achievable ergodic secrecy rate (ESR) is derived when taking into account the feedback overhead. The secrecy performance of the proposed scheme is evaluated through simulations in terms of ESR and bit error rate (BER). The influences of the key parameters such as the power allocation ratio and the feedback duration are also discussed.

Session Chair

Ning Zhang, Jiliang Li

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Session CIS-05

Attack Detection

Conference
3:10 PM — 4:40 PM CST
Local
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

Multi-Attacker Multi-Defender Interaction in mMTC Networks Via Differential Game

Qiuyue Gao, Huici Wu, Jiazhen Zhang and Yunfei Zhang (Beijing University of Posts and Telecommunications, China); Ning Zhang (Texas A&M University-Corpus Christi, USA); Xiaofeng Tao (Beijing University of Posts and Telecommunications, China)

0
Massive machine type communications (mMTC) scenario, as one of the typical application scenarios of the fifth generation (5G) and beyond, introduces numerous communication terminals into networks, which inflicts the expansion of network attack surface, and poses tremendous challenges to network security. Different from single attacker and single defender interaction, a large volume of terminals in mMTC scenario may form attack or defense alliances, i.e., the interaction exists between multiple attackers and multiple defenders. This paper focuses on the multi-attacker multi-defender interaction in the mMTC scenario. Firstly, a non-zero-sum multi-attacker and multi-defender differential game model is formulated, considering attack and defense alliances, the dynamics and continuity of network interaction and the real-time strategy selection. Then, an optimal multi-attacker and multi-defender strategy selection algorithm is proposed by the introduction of Hamilton functions, based on which the optimal saddle point strategy is obtained. Finally, simulation results demonstrate the evolution of the optimal attack and defense strategies and show the impact of cost coefficient in the proposed differential game model on the attack and defense evolution. It is revealed that both alliances tend to be preemptive and the alliances will appropriately increase the intensity of attack and defense as the cost increases.

Relay-Aided Proactive Eavesdropping with Learning-Based Power and Location Optimization

Rui Ma (Chongqing University & Center of Communication and Tracking Telemetry Command, China); Haowei Wu, Jinglan Ou and Zhengchuan Chen (Chongqing University, China); Qihao Peng (School of Microelectronics and Communication Engineering, China)

1
This work investigates a relay-aided proactive eavesdropping network, where a legitimate monitor eavesdrops on communication between suspicious users via a friendly amplify-and-forward full-duplex (FD) relay. The closed-form expressions are derived, including the decoding outage probability for the suspicious link, the eavesdropping non-outage probability for the eavesdropping link, and the average eavesdropping rate (AER). To maximize the AER, separate optimization problems for the power and location of the relay are settled, employing the good fitting characteristic of the deep feedforward neural network. Then, a low-complexity learning-based iterative algorithm is proposed to solve the joint optimization problem. Numerical results demonstrate the effectiveness and optimality of the proposed algorithm, and show that the optimized FD relay-aided proactive eavesdropping scheme outperforms the existing benchmark schemes in terms of the AER.

Delay-aware Secure Transmission in MEC-enabled Multicast Network

Qian Xu and Pinyi Ren (Xi'an Jiaotong University, China)

0
This paper studies the joint precoding design and computation offloading in a mobile-edge-computing-enabled (MEC-enabled) multicast communication network. Physical layer security is exploited to protect the privacy of the transmitted messages from being obtained by the users outside the multicast group, i.e., the eavesdroppers. Specifically, the base station (BS) equipped with a MEC server needs to deliver the compressed confidential document to the desired users in a multicast group. The compressed document can be partially decompressed at the MEC before downlink transmission, and the remaining decompression task is performed at the users. To guarantee the security of downlink transmission, beamforming and artificial noise (AN) are employed at the BS. Considering the energy constraints at the BS and users, a delay minimization problem is formulated, which minimizes the delay for both wireless transmission and document decompression. By leveraging on the one-dimensional search and successive convex approximation, an efficient solution including beamforming/AN design and computation offloading is obtained. Numerical examples are provided to evaluate the delay performance under different system parameters.

A Fast Method to Attack Real-time Object Detection Systems

Yiwei Li, Guoliang Xu and Wanlin Li (Chongqing University of Posts and Telecommunications, China)

0
With the development of deep learning, image and video processing plays an important role in the age of 5G communication. However, deep neural networks are vulnerable: subtle perturbations can lead to incorrect classification results. Nowadays, adversarial attacks on artificial intelligence models have seen increasing interest. In this study, we propose a new method named FA to generate adversarial examples of object detection models. Based on the generative adversarial network (GAN), we combine the classification and location information to make the generated image look as real as possible. Experimental results on the PASCAL VOC dataset show that our method efficiently and quickly generates the image. Then, we test the transferability of adversarial samples on different datasets and object detection models such as YOLOv4, which also achieve certain transfer performance. Our work provides a basis for further exploring the defects of deep learning and improving the robustness of the systems.

Distributed Denial of Service Defense in Software Defined Network Using OpenFlow

Pengfei Zhai and Chungang Yang (Xidian University, China)

0
Software-Defined Network (SDN) is a new type of network architecture, whose innovation lies in decoupling the traditional closed network system into a control plane, a data plane, and an application plane. It logically implements centralized control and management of the network, and SDN is considered to represent the network development trend. However, SDN still faces many security challenges. Currently, the number of insecure devices is huge. Distributed Denial of Service (DDoS) attacks are one of the major network security threats. We focuse on the detection and mitigation of DDoS attacks in SDN. Firstly, we explore a solution to detect DDoS using Renyi entropy, and we use exponentially weighted moving average algorithm to set a dynamic threshold to adapt to the network changes. Second, to mitigate this threat, we analyze the historical behaviors of each source IP address and score it to determine the malicious source IP address, and use OpenFlow protocol to block attack source. The experimental results show that the presented scheme can effectively detect and mitigate DDoS attacks.

Session Chair

Huici Wu, Yilong Hui

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Session CT-04

Resource Allocation

Conference
3:10 PM — 4:40 PM CST
Local
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

Radio Resource Allocation for RAN Slicing in Mobile Networks

Liushan Zhou and Tiankui Zhang (Beijing University of Posts and Telecommunications, China); Jing Li (Network Technology Research Institute of China Unicom, China); Yutao Zhu (Beijing University of Posts and Telecommunications, China)

0
Network slicing is a key technology for addressing the issue of differentiated performance requirements of diversified services in mobile networks. We focus on the radio resource allocation for RAN slicing to ensure the isolation between slices, and improve radio resource utilization. This paper proposes a radio resource allocation algorithm for Service Level Agreement (SLA) contract rate maximization. Firstly, the business parameters in SLA are mapped to the measurable network performance metrics. Then, radio resources are allocated to network slices on the basis of the collected SLA requirements. Meanwhile, Radio resources of slices that do not meet the requirements are dynamically updated without affecting the performance of slices which has met the SLA requirements, to maximize the SLA contract rate of all slices. The simulation results show that the algorithm can achieve a better SLA contract rate on the premise of ensuring isolation between slices, additionally increase the number of service users.

Communication and Computation Resource Allocation for End-to-End Slicing in Mobile Networks

Zhou Tong, Tiankui Zhang and Yutao Zhu (Beijing University of Posts and Telecommunications, China); Rong Huang (Network Technology Research Institute of China Unicom, China)

0
In the mobile network, to better meet various vertical industries applications, network slicing is required in both the mobile core network and the access network to achieve end-to-end network slicing. This paper proposes a framework of end-to-end mobile network slicing. We model an end-to-end delay minimization problem based on the proposed framework. Then we propose a communication and computation resource joint allocation algorithm in end-to-end network slicing for ultra-reliable low-latency communication (URLLC). In the process of network slice orchestration and deployment, the virtual network function (VNF) mapping in the core network and the wireless resource allocation in the access network are jointly considered to minimize the end-to-end delay that ensures the reliability of the network slice. The simulation results show that the proposed algorithm can effectively reduce the end-to-end latency of network slicing, and guarantee the reliability requirements of network slicing as well.

Robust Design for signal mismatch with Steering Vector and Covariance Matrix Constraints

Junhui Qian, Shuya Zhang and Mengchen Lu (Chongqing University, China); Fengchun Tian (College of Communication Engineering£¬Chongqing University, China)

0
In this paper, a novel robust design algorithm for signal mismatch with steering vector and covariance matrix constraints is developed. The mismatched desired signal steering vector is estimated based on maximizing desired signal output power under the correlation coefficient constraint and norm constraint. The original nonconvex problem is proved belong to hidden convex, whose relaxation is tight, and can be solved by the relaxed semidefinite programming (SDP) method. Subsequently, the interference-plus-noise covariance matrix is further restructured with the shrinkage method and the subspace theorem based on the corrected steering vector, whose efficiency is analytically proven. Numerical experiments show that the proposed method gives satisfactory results under different scenarios.

Time-frequency Overlapped Signals Intelligent Modulation Recognition in Underlay CRN

Huaiyu Tang and Mingqian Liu (Xidian University, China); Liwei Chen (Luoyang Electronic Equipment Test Center of China, China); Jianying Li and Jian Chen (Xidian University, China)

0
In underlay cognitive radio networks (CRN), a novel intelligent modulation recognition method of time-frequency overlapped MPSK/MQAM signals based on convolutional neural network (CNN) is proposed in this paper. Aiming to achieve better modulation recognition performance of the time-frequency overlapped signals, the contour maps of cyclic spectrum of the received signals as the initial features are extracted, and then the CNN is used to extract further features as the final features. After preprocessing the contour maps to serve as input data for the CNN, the CNN is constructed and optimized to complete the training of the CNN. Finally, the well trained CNN is used for modulation recognition of the time-frequency overlapped MPSK/MQAM signals. Simulation results show that the average recognition rate of the proposed method is 90% when the signal-to-noise ratio (SNR) is 2dB, and it is robust to the power ratio and spectrum overlapped rate of the component signals.

Joint Optimization of Wireless Resource Allocation and Task Partition for Mobile Edge Computing

Zhuo Yang (Chongqing University, China); Jinfeng Xie (Chongqing Academy of Informatioon and Communications Technology, China); Jie Gao, Zhixiong Chen and Yunjian Jia (Chongqing University, China)

0
Mobile edge computing (MEC) is a promising technology to provide computing services for resource-constrained mobile users, improving the computing experience. However, energy consumption is also a considerable expense for users. In this paper, we investigate the joint wireless resource allocation, power control and computation offloading for a MEC-based multi-user wireless communication system. The objective of this paper is to minimize the energy consumption of all users. We formulate the problem as an optimization problem and propose to solve the optimal power control strategy by using convex optimization method. Then we derive the semi-closed forms of resource allocation and computation offloading. By using dichotomy algorithm, we have obtained the resource allocation and computation offloading policy. It is shown by simulation results that the proposed scheme can effectively reduce the energy consumption of users.

Session Chair

Junhui Qian, Yunjian Jia

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Session IoT-06

Localization, Recognition and Detection

Conference
3:10 PM — 4:40 PM CST
Local
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

Indoor CSI fingerprint localization based on tensor decomposition

Yuexin Long (Chongqing University of posts and Telecommunications, China); Liang Bo Xie, Mu Zhou and Yong Wang (Chongqing University of Posts and Telecommunications, China)

0
Indoor Wi-Fi localization methods based on the Received Signal Strength (RSS) are widely used because of the low computational complexity and strong applicability. Compared with the RSS, the Channel State Information (CSI) can provide the multi-channel subcarrier phase and amplitude information to better describe the signal propagation path. Thus, the CSI becomes one of the most commonly used signal features in indoor Wi-Fi localization. Compared to the CSI-based geometric localization method, the fingerprint-based localization method has advantages of easy implementation and high accuracy. Based on this, this paper proposes an indoor CSI fingerprint localization approach based on tensor decomposition. Specifically, we combine the tensor decomposition algorithm based on the Parallel Factor (PARAFAC) analysis model with the Alternating Least Squares (ALS) iterative algorithm to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, distinguishing from the traditional localization algorithm based on machine learning, this paper establishes a localization model based on the Partial Least Squares Regression (PLSR) algorithm to predict position coordinates. Experimental results show that the proposed approach is with the high localization accuracy and good fingerprint collection efficiency.

A Novel Cost-Effective IoT-Based Traffic Flow Detection Scheme for Smart Roads

Zhao Liu, Changle Li, Hui Wang, Yunpeng Wang, Yilong Hui and Guoqiang Mao (Xidian University, China)

1
Autonomous driving is expected to be realized in the future with the development of information and communication technology (ICT). However, the reliability of autonomous vehicles (AVs) in complex environments needs major improvement. To facilitate the autonomous driving, high-precision and low-cost traffic flow detection is essential for driving decision and traffic surveillance, and has drawn increasing attention from both academia and industry. In this paper, we propose a novel cost-effective IoT-based traffic flow detection scheme with particular focuses on vehicle counting and speed measurement. To this end, microwave Doppler radar sensors with a unit price of $2.3 are utilized to collect the traffic data of passing vehicles on the road. Then, a multi-threshold detection algorithm is proposed to extract features for vehicle counting and speed measurement. After this, experiments are carried out in different scenarios to evaluate the proposed traffic flow detection scheme. The results validate the high-precision detection with average 98.3% vehicle counting accuracy and 95.8% speed measurement accuracy.

Basketball Footwork Recognition using Smart Insoles Integrated with Multiple Sensors

Min Peng and Zhong Zhang (Hefei University of Technology, China); Qingfeng Zhou (Dongguan University of Technology,China)

1
In the basketball training, statistical data of basketball footwork can be used to improve training level.However, most basketball motion recognition systems are with high cost, and the recognition of footwork is often overlooked.In this paper, we propose a system to recognize basketball footwork using smart insoles. The system collects data through the three-axis accelerometer and three-axis angular velocity meter embedded in smart insoles.Then, five kinds of basic basketball footwork such as sideslip step, back step, cross step, jab step and jump step can be recognized.In this paper, K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are used for footwork recognition.The experimental results show that the proposed system can recognize five kinds of basketball footwork effectively.

A Dynamic Continuous Hand Gesture Detection and Recognition Method with FMCW Radar

Aihu Ren, Yong Wang, Xiaobo Yang and Mu Zhou (Chongqing University of Posts and Telecommunications, China)

0
In this paper, a continuous dynamic hand gesture detection and recognition method is proposed using a frequency modulated continuous wave (FMCW) radar. Specifically, we collect the raw radar data to estimate the radar intermediate frequency (IF) signal, and construct the range-time map (RTM) and Doppler-time map (DTM) with 2-Dimensional Fast Fourier Transform (2D-FFT). Then, we propose a hand gesture detection method, which obtains the amplitude of the normalized hand gesture and uses a threshold to effectively segment the continuous hand gesture. Finally, the hand gesture is recognized by the proposed Fusion Dynamic Time Warping (FDTW) algorithm based on the central time-frequency trajectory. Experiments with radar data show that the accuracy of the proposed hand gesture detection method can reach 96.17%, and compared with the traditional recognition algorithm, the proposed recognition algorithm can significantly improve the recognition accuracy rate (hand gesture average recognition accuracy rate can reach 94.50%) with the time complexity reduced by more than 50%.

Intelligent Emotion Detection Method in Mobile Edge Computing Networks

Zhidu Li, Ji Lv and Dapeng Wu (Chongqing University of Posts and Telecommunications, China)

0
How to detect emotions of different people in time is significant in various areas, such as healthcare, VR/AR and etc. In this paper, an intelligent emotion detection method is proposed to transmit individual emotion data in real-time with as little energy as possible in a resource-limited mobile edge computing (MEC) networks. First, a data compression method is designed based on Convolutional Auto-Encoder (CAE) to compress emotion data on the user side. Then, to guarantee the requirements of data transmission delay and the data distortion rate simultaneously, an optimal channel allocation algorithm is proposed. Thereafter, the emotion data is recovered and analyzed by the edge personal emotion model EEGNET on the edge computing server where the real-time emotions of users can be monitored. The effectiveness of the proposed method is finally verified by extensive simulation experiments.

Session Chair

Xiaozheng Gao, Hang Yuan

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Session MWN-06

Security

Conference
3:10 PM — 4:40 PM CST
Local
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

Secure Routing Based on Geographic Location for Resisting Blackhole Attack In Three-dimensional VANETs

Jingxuan Lyu and Chenju Chen (Beijing University of Posts and Telecommunications, China); Hui Tian (Beijng university of posts and telecommunications, China)

0
Based on dynamic 3D Vehicle Ad Hoc Networks (VANET) environment, this paper puts forward Trust-based Greedy Forwarding Routing (TGF),including establishing trust model to resist black hole attack and selecting the secure routing path. Compared with the two-dimensional VANETs, the problem of transmission loss is considered in the three-dimensional environment. And in the process of establishing the secure route, the influence of geographic location and the number of interactions between nodes is considered. Compared with the Greedy Perimeter Stateless Routing (GPSR), the packet loss rate reduces approximately 24%, and the throughput increases approximately 18%. The NS3 simulation verifies that TGF is more suitable for three-dimensional scenarios with black hole attack than GPSR.

Adaptive Relay Selection with Physical Layer Security for Green Communicaions

Yanjun Yao (No. 38 Research Institute of China Electronic Technology Corporation, China)

0
This paper investigates the issue of physical layer security for green communications. Specifically, the cooperative communication scenario which contains one source node, one eavesdropper, one destination and multiple cooperative nodes is considered. We aim at minimizing the total power consumption under the restriction of security performance. To achieve this goal, two novel cooperation strategies are proposed. The two methods are named as Only Relay and Relay and Jamming Pair. The former selects one relay node for cooperation. However, as the legitimate receiver's channel condition deteriorates, in order to satisfy security performance, the system would switch to the latter method, which selects a relay and jamming node pair for cooperation. The optimum node selection and power allocation method under the two strategies is studied. The performance of minimal power consumption under different circumstances is investigated. Numerical simulation results show that compared to traditional communication without cooperative node, the proposed methods result in less power consumption under security restriction.

Improving the Security of Wireless Network from Software Perspective

Xiaoxue Wu and Weiqiang Fu (Northwestern Polytechnical University, China); Dejun Mu (College of Automation, Northwestern Polytechnical University, China); Deming Mao, Hui Zhang and Wei Zheng (Northwestern Polytechnical University, China)

0
To guarantee the security of the wireless network, effort must be paid to software security as software plays a more and more important role in the development of wireless network intelligentization. Security bug reports (SBRs) are bug reports that describe the security issues of software systems. Cross-project SBR prediction, which uses a prediction model trained with labeled data from one project to predict another project, has been proposed to eliminate SBRs of software products. While reviewing the previous work focused on cross-project SBR prediction, we find the performance (e.g., Recall, F1-score) of cross-project SBR prediction is too low to the production application. This paper proposes a hybrid sampling approach based on text similarity and uncertainty-sampling. We conduct experiments on ten publicly available datasets. The results show our approach could significantly improve the performance of cross-project SBR prediction. On average, the performance of the classification model can be improved by 34%, 64%, 48%, and 11% in terms of Recall, Precision, F1-score, and AUC, respectively.

Secrecy Performance Analysis in Internet of Satellites: Physical Layer Security Perspective

Yongpeng Shi (Luoyang Normal College, China); Ya Gao (Luoyang Normal University, China); Yujie Xia (Luoyang Normal College, China)

0
As the latest evolving architecture of space networks, Internet of Satellites (IoSat) is regarded as a promising paradigm in the future beyond 5G and 6G wireless systems. However, due to the extremely large number of satellites and open links, it is challenging to ensure communication security in IoSat, especially for wiretap resisting. To the best of our knowledge, it is an entirely new problem to study the security issue in IoSat, since existing works concerning physical layer security (PLS) in satellite networks mainly focused on the space-to-terrestrial links. It is also noted that, we are the first to investigate PLS problem in IoSat. In light of this, we present in this paper an analytical model of PLS in IoSat where a terrestrial transmitter delivers its information to multi-satellite in the presence of eavesdroppers. By adopting the key parameters such as satellites' deployment density, minimum elevation angle, and orbit height, two major secrecy metric including average secrecy capacity and probability are derived and analyzed. As demonstrated by extensive numerical results, the presented theoretical framework can be utilized to efficiently evaluate the secrecy performance of IoSat, and guide the design and optimization for communication security in such systems.

Session Chair

Xuefang Li, Rong Chai

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Session WCS-09

Advanced Wireless Techniques III

Conference
3:10 PM — 4:40 PM CST
Local
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

A Parallel Carrier Recovery Scheme for an 8Gbps Terahertz Communication System

Yuli Wang, Zhen Qin, Yunsi Ma, Yuanjing Qi and Nan Wu (Beijing Institute of Technology, China)

0
In this paper, a parallel carrier recovery scheme with a large frequency acquisition range of about ¡À50% of the symbol rate is proposed for terahertz communication systems. The proposed scheme is divided into two parts: the frequency offset acquisition based on Mengali and Morelli (M&M) algorithm and the frequency offset tracking employing a decision feedback phase-locked loop. Then, we also design its efficient hardware implementation architecture on Field Programmable Gate Array (FPGA) platform. To improve the utilization of hardware resources, two independent frequency offset compensation modules of the acquisition stage and of the tracking stage are integrated into one offset compensation module. Moreover, an 8 Gbps communication system relying on the proposed parallel carrier recovery scheme is implemented on a Zynq UltraScale+ RFSoC ZUC111 Evaluation Kit. The simulation and implementation results show that the proposed parallel carrier recovery scheme performs well in large carrier frequency offset scenarios and its bit error rate (BER) performance can approach the theoretical curve under 16-ary quadrature amplitude modulation (16-QAM).

Sparse Code Multiple Access with Index Modulation

Chen Zuo and Jianping Zheng (Xidian University, China)

0
In this paper, we study the application of index modulation (IM) to uplink sparse code multiple access (SCMA), and present a scheme called SCMA-IM. In the proposed SCMA-IM, the candidate resources of one user are partitioned into two groups. All the resources in the first group are utilized to transmit signals, and only part of resources in the second are activated through IM to transmit signals. The signals transmitted in these resources are jointly designed according to the same codebook design method as SCMA. Moreover, an effective message passing algorithm is presented to perform the multiuser detection. Simulation results show that the proposed SCMA-IM can achieve better performance than conventional SCMA when the number of receive antenna is large.