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IEEE Conference on Wireless Communications and Networking

作者： Jiaxin Du Wenbo Guo Mu Yan Hongzhi Zhao Shihai Shao National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The space-air-ground integrated networks (SAGNs) are promising components of the next generation communication system, and unmanned aerial vehicle (UAV) swarms are the primary infrastructures of the SAGNs aerial layer, used for relaying information. Furthermore, UAV swarms form virtual antenna arrays (VAAs) and implement collaborative beamforming (CB), which can improve the deep-space communication distance and expand the ground coverage. However, in practical engineering, due to the doppler effect and local oscillator random frequency drift, there is a frequency offset between UAVs, even after frequency synchronization, resulting in CB performance degradation. Therefore, in the presence of frequency offset, this paper investigates and presents the derivation of the average far-field beampattern, 3-dB beamwidth, and complementary cumulative distribution function (CCDF) for UAV swarm executing CB in SAGNs. The results show that the average far-field beam-pattern, peak power, 3-dB beamwidth, and CCDF, deteriorate as frequency offset intensifies. Furthermore, as the UAV swarm scale increases, the peak power attenuation is aggravated, and the decrease in CCDF is steeper. With the increase in the UAV swarm distribution range, the 3-dB beamwidth expansion is alleviated.

关键词： Local oscillators Array signal processing Collaboration Autonomous aerial vehicles space-air-ground integrated networks Attenuation Doppler effect Frequency synchronization Next generation networking Antenna arrays

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Feature Alignment Transformer for Cross-domain Radio Frequency Fingerprint Identification 24

Feature Alignment Transformer for Cross-domain Radio Frequen...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Wang, Yatong Wen, Zhongyi Cao, Bin Yan, Mu Song, Changqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China University of Electronic Science and Technology of China Chengdu611731 China Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798350363760

Radio Frequency Fingerprint Identification (RFFI) has emerged as an effective device authentication paradigm, offering non-cryptographic, tamper-proof, and lightweight solutions for IoT networks. However, conventional deep learning-based RFFI schemes often experience significant performance degradation in cross-domain scenarios due to the distribution shift between training and test datasets, known as the cross-domain RFFI problem. To address this issue, we propose FATransformer, a novel framework for cross-domain RFFI, which comprises four key modules: data preprocessing, transformer backbone, feature alignment, and automatic tuning. Additionally, we establish a theoretical relationship between target domain error bounds and domain discrepancies to support the design of the feature alignment method. Extensive experiments on real-world datasets demonstrate FATransformer's superior accuracy in multiple cross-domain RFFI tasks compared to baselines. © 2024 IEEE.

关键词： Deep learning

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Waveform Design for ISAC Systems Via the Riemannian Geometric Optimization Network

Waveform Design for ISAC Systems Via the Riemannian Geometri...

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European Signal Processing Conference (EUSIPCO)

作者： Weijie Xiong Jingran Lin Kai Zhong Qiang Li Jinfeng Hu University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this paper, we focus on designing a dual-functional constant modulus (CM) waveform for integrated sensing and communication (ISAC) systems. The design aims to match a desired beampattern for radar sensing and minimize transmission distortion for communication. Due to the constant modulus constraint (CMC), the problem is non-convex and thus difficult to solve. Among current studies, the Riemannian conjugate gradient (RCG) method can effectively address the CMC problem with notable performance. However, the existing RCG method utilizes constant step sizes and scalar parameters during optimization, which often leads to results falling into a bad local optimum, consequently degrading both radar detection ability and communication quality. To alleviate these drawbacks, we develop a model-driven network named the Riemannian geometric optimization network (RGON). It is constructed by unfolding the iterative RCG method into the network layers. In the network, we transform the step sizes and scalar parameters into learnable parameters and learn their values in an unsuper-vised manner. Through this adaptive learning, the network can obtain the most appropriate step sizes and scalar parameters for each iteration. Numerical results show that the proposed network with trainable step sizes and scalar parameters achieves better performance in terms of convergence speed, beampattern matching and average sum rate compared with other related methods.

关键词： Adaptation models Design methodology Radar detection Europe Transforms Radar Integrated sensing and communication Distortion Iterative methods Optimization

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Domain Adaptation-based Radio Frequency Fingerprint Identification for Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Wang, Yatong Cao, Bin Wen, Zhongyi Yan, Mu Song, Changqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100191 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China

With the widespread adoption of cloud and fog platforms, security issues facing Industrial Cyber-Physical Systems (ICPSs) are increasingly prominent. Radio Frequency Fingerprinting (RFFI) emerges as a promising physical layer security technique for user authentication, effectively enhancing ICPS security over wireless networks by exploiting inherenthardware imperfections in transmitters. However, as technologies evolve and user environments become denser and more complex, existing RFFI techniques encounter significant domain shift challenges, leading to performance degradation. Therefore, developing domain-adaptive RFFI techniques is crucial for the effectiveness of ICPSs. In this article, we first provide a comprehensive review of the RFFI framework, emphasizing its applications in ICPSs and the key processes involved. Next, we focus on the domain shift issues faced by RFFI in ICPSs and present a comprehensive survey of domain adaptation methods. To tackle these challenges, we propose a novel feature-alignment based domain adaptation (FADA) framework for RFFI, which is designed to mitigate the performance degradation caused bycross-domain shifts by aligning the feature representations of the source and target domain. Finally, we discuss the inherent challenges and future directions for developing domain adaptation based RFFI to enhance the security of ICPSs. © 2025 IEEE. All rights reserved.

关键词： Radio frequency identification (RFID)

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Constant Modulus IRS-Assisted Secure Beamforming for Massive MIMO

Constant Modulus IRS-Assisted Secure Beamforming for Massive...

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IEEE International Conference on Communications Workshops, ICC

作者： Weijie Xiong Yuhan Zhang Jingran Lin Kai Zhong Jinfeng Hu University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The physical layer (PHY) security is a key technology for constant modulus (CM) massive MIMO systems. In this paper, we consider using an intelligent reflecting surface (IRS) to provide the system with additional degrees of freedom, thus enhancing its performance. We approach the problem from an optimization perspective, aiming to maximize the security rate. The optimization problem presents significant challenges due to highly coupled variables, non-convex constraints and the non-concave objective function. To tackle these challenges, we propose an efficient alternating optimization (AO) method. This method guarantees convergence to a Karush-Kuhn-Tucker (KKT) point and separately optimizes the CM beamforming and phase shift coefficients at the IRS as two sub-problems. Both sub-problems are solved using similar frameworks of the Dinkelbach method and the alternating direction method of multipliers (ADMM). Simulation results show that the proposed method outperforms many other related methods.

关键词： Array signal processing Simulation Conferences Massive MIMO Physical layer Linear programming Convex functions

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Decoupled Online Learning Network Method for Analog Beamforming with Antenna Selection

Decoupled Online Learning Network Method for Analog Beamform...

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Ubiquitous Communication (Ucom), International Conference on

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

The analog beamforming design with antenna selection from a beampattern matching perspective is a key technology in large-scale antenna arrays (LSAAs) wireless communication systems. Currently, most existing methods use the scaled analog beamforming (SAB) architecture with phase shifters (PS) at the transmitter side, leading to constrained degrees of freedom. To enhance performance, we leverage the phase-corrected analog beamforming (PCAB) architecture with PS both at the transmitter side and the receiver side. We further formulate the problem as a mixed-integer least-squares problem with both constant modulus constraints (CMCs) and binary constraints (BCs). Due to the CMCs and BCs, the problem is non-convex. Additionally, the coupling effect between variables further aggravates the difficulty. To handle the problem, a decoupled online learning network is proposed. In the network, we address the BCs by transforming them into a continuously differentiable function with different biases. Simultaneously, we maintain parameters under CMCs by representing them in exponential form and leveraging their phases. By transforming the biases and the phases into learnable parameters, the loss function is minimized by the deep learning optimizer.

关键词： Wireless communication Deep learning Couplings Array signal processing Transmitters Phase shifters Receivers Antenna arrays

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Millimeter-Wave Full-Duplex Phased Array RF Self-Interference Cancellation technology

Millimeter-Wave Full-Duplex Phased Array RF Self-Interferenc...

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IEEE International Conference on Communications Workshops, ICC

作者： Yang Li Wensheng Pan Gezu Jiwu Chengzhe Shi Nanzhou Hu Shihai Shao National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In the integrated access and backhaul (IAB) base stations, a model of large-scale antenna arrays is established to address the problem of radio frequency self-interference cancellation (RFSIC) in millimeter-wave (mmW) full-duplex (FD) systems. The characteristics of the SI under the condition of separating the receiving and transmitting antenna arrays are analyzed. This paper simplifies the RFSIC model using the equivalent delay method, while an RFSIC model applicable to the received beamforming synthesis is proposed. The RFSIC performance under different tap numbers is also compared by simulation experiments. The simulation results show that the RFSIC ability of the 16 taps is greater than 50 dB for the SI with a carrier frequency of 26.5 GHz and a bandwidth of 200 MHz. Furthermore, the proposed method is tested and verified in a real scenario, and the test results show that the RFSIC ability is greater than 35 dB when 16 taps are used for SI reconstruction. Combined with digital self-interference cancellation (DSIC), the error vector magnitude (EVM) can be effectively increased by 30.8 dB, while the phased array system can realize FD communication.

关键词： Phased arrays Interference cancellation Simulation Conferences Millimeter wave technology Full-duplex system Bandwidth

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Community Search Based on Containment control of Multi-Agent System with Opinion Leaders 43

Community Search Based on Containment Control of Multi-Agent...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Siyang Li, Tong Shao, Jinliang Zheng, Wei Xing University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9789887581581

Community search is the problem of identifying the community in which a given node resides. Different from traditional community search methods based on specific topological structures, this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community formation. Firstly, the Breadth-First Search method is used to locally sample the network. Secondly, containment control of multi-agent system is applied to obtain the final state of each node for local community partitioning. Finally, grounded in practical considerations, an effective leadership transfer mechanism is proposed to update the results and enhance search precision. Through experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms, the effectiveness and rationality of our algorithm are demonstrated. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Joint Admission control and Beamformer Design for Mobile Users: Stay Here or Move to a Better Position?

Joint Admission Control and Beamformer Design for Mobile Use...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingran Lin Weijie Xiong Qiang Li Xiangze Kong Yuhan Zhang University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

In this paper, we study the joint admission control and beamforming problem within a network where one multi-antenna base station tries to serve multiple single-antenna users. Unlike most existing studies which merely identify the users that should be denied, our work further suggest better positions in the neighbouring area for them where the previously-rejected users are allowed to access the network. To address this, we assume the knowledge of channel vectors within the network coverage area, and then jointly optimize the transmit beamformer and the channel vector associated with each user to minimize the network power cost, with a penalty measuring the mismatch between the optimized channel and the reference channel with current user position. Specifically, a non-zero mismatch means that the corresponding user is inadmissible at present, but may access network if moving to the position with the channel closest to the optimized result. Basically, this is a challenging non-convex problem, and we design a penalty dual decomposition (PDD)-based algorithm to iteratively achieve a stationary solution. The algorithm is highly efficient since a simple analytical solution is derived in each step.

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A Digitally assisted nonlinearity suppression architecture using over-the-air beam cancelation

A Digitally assisted nonlinearity suppression architecture u...

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作者： Mu, Qin Li, Nanxi Chen, Cong Wei, Xiaozheng Wen, Rong Wu, Muqing Li, Xiang Liu, Ying School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China National Key Lab of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China School of Network and Communication Engineering Chengdu Technological University Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

Using a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to generate a nonlinear cancelation signal to cancel with the main beam at the receiving end. We explore a two-step method to model the nonlinear signal and combine neural network to complete the modeling. In addition, we introduce a novel method for estimating and tracking the channel to update the model coefficients. To verify the proposed methods, we design a scene and compare the performance of our architecture with that of conventional DPD. Our experiments demonstrate that our proposed methods can significantly enhance the adjacent channel power leakage ratio performance by 12 dB while also optimizing in-band distortion. Moreover, our architecture outperforms conventional DPD approaches even with enhanced distortion. © 2023 John Wiley & Sons Ltd.

关键词： Power amplifiers

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