Signal Processing for Communications

Session SPC-04


10:30 AM — 12:00 PM CST
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)

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)

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)

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)

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)

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

Session SPC-05

Coding and Decoding

1:30 PM — 3:00 PM CST
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)

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)

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)

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)

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)

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

Session CIS-05

Attack Detection

3:10 PM — 4:40 PM CST
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)

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)

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)

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)

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)

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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