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Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO networks

IEEE TRANSACTIONS ON wireless communicationS 2024年第10期23卷 14125-14139页

作者： Li, Zhiyang Goettsch, Fabian Li, Siyao Chen, Ming Caire, Giuseppe Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Peoples R China Tech Univ Berlin Fac EECS D-10587 Berlin Germany Univ Alaska Anchorage Dept Elect Engn Anchorage AK 99508 USA

We consider scalable cell-free massive multiple-input multiple-output networks under an open radio access network paradigm comprising user equipments (UEs), radio units (RUs), and decentralized processing units (DUs). UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors (implementing the physical layer for each user) are hosted by the DUs as software-defined virtual network functions. Unlike the current literature, mainly focused on the characterization of the user rates under unrestricted fronthaul communication and computation, in this work we explicitly take into account the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. In particular, we systematically address the new problem of joint fronthaul load balancing and allocation of the computation resource. As a consequence of our new optimization framework, we present representative numerical results highlighting the existence of an optimal number of quantization bits in the analog-to-digital conversion at the RUs.

关键词： network topology Quantization (signal) Program processors Optimization Load management wireless communication Resource management Cell-free massive MIMO limited fronthaul O-RAN load balance

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Achieving Covertness and Security in Broadcast Channels With Finite Blocklength

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IEEE TRANSACTIONS ON wireless communicationS 2022年第9期21卷 7624-7640页

作者： Wang, Chao Li, Zan Zhang, Haibin Ng, Derrick Wing Kwan Al-Dhahir, Naofal Xidian Univ Integrated Serv Networks Lab Xian 710071 Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Networ Nanjing 210003 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

Considering multi-user downlink ultra-high reliability and low latency communications (URLLC), this paper employs the artificial noise (AN) technique to establish a secure and covert broadcast communication paradigm for the first time. Specifically, a multi-antenna transmitter (Alice) broadcasts the confidential information to multiple legitimate users in the presence of a multi-antenna malicious warden (Willie) and a multi-antenna eavesdropper (Eve). It is well known that AN is an effective technique for securing the physical layer security (PLS) of signal transmissions. Nevertheless, AN emission also exposes the signal transmission and decreases the signal covertness. Taking into account the impact of short-packet URLLC transmissions, we investigate the joint optimization of the precoder and AN to maximize the secrecy rate under the covertness constraint. Although the considered problem is nonconvex, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve it optimally. However, the nested-loop structure of the BRB-based algorithm incurs a high computational complexity. To strike a balance between the performance and computational complexity, we also propose a low-complexity penalty successive convex approximation (SCA)-based algorithm, whose performance approaches that of the optimal BRB-based algorithm, particularly in the low to medium transmit power regime. Simulation results demonstrate the excellent performance of our proposed optimization algorithms compared with various benchmark algorithms and unveil the importance of exploiting AN for secrecy provisioning.

关键词： Ultra reliable low latency communication Security Downlink Optimization MIMO communication Error probability Approximation algorithms URLLC physical layer security covert communication branch-reduce-and-bound penalty successive convex approximation

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A Novel Grant-Free Random Access Scheme for mMTC 23

A Novel Grant-Free Random Access Scheme for mMTC

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23rd IEEE International Conference on communication technology, ICCT 2023

作者： Zhao, Xuyuan Liu, Yang Wang, Cheng-Xiang School of Internet of Things Engineering Jiangnan University Wuxi214122 China Shanghai Institute of Microsystem and Information Technology Key Laboratory of Wireless Sensor Network and Communication Shanghai200050 China School of Information Science and Engineering Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350325959

Grant-free (GF) random access is a promising technique to support massive machine-type communication (mMTC) systems. Due to the conflicts, the performance is degraded. The repeated transmission of packets reduces the probability of conflict. In this paper, we propose a novel GF random access scheme with retransmissions. An optimal preamble allocation method is proposed to determine the size of the retransmission preamble pool. Numerical simulations are performed to validate the theoretical analysis. The proposed scheme adapts to various mMTC scenarios. © 2023 IEEE.

关键词： grant-free(GF) performance analysis preamble allocation retransmissions

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Defeating CSI obfuscation mechanisms: A study on unauthorized Wi-Fi Sensing in wireless sensor network

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COMPUTER networkS 2025年 263卷

作者： Chu, Zhiming Li, Guyue Meng, Qingchun Li, Haobo Zeng, Yuwei Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Univ Dundee Sch Sci & Engn Dundee DD14HN Scotland Minist Educ China Key Lab Cyberspace Secur Henan Key Lab Cyberspace Situat Awareness Zhengzhou 450001 Peoples R China

The proliferation of Wi-Fi sensing technology has raised significant privacy concerns due to potential unauthorized environmental monitoring. As a typical countermeasure, the Channel State Information (CSI) fuzzer uses a time-varying filter at the transmitter to obfuscate CSI, allowing only legitimate receiver who has the pre-shared filter parameters as keys to restore the original CSI. In this work, we present SnoopFi, a framework enabling unauthorized reconstruction of environment-matching sensing signals from obfuscated CSI, even with limited training samples. SnoopFi acquires accurate raw CSI when attackers exploit security vulnerabilities to obtain keys. It can also generate a new base signal that reflect the physical environment for sensing when the attackers' capabilities are limited. SnoopFi employs two strategies to negate the filter's effects: (1) The attacker first attempts to guess the keys, and then it inverts the filter by modeling the nonlinear relationship between the filter's response and the keys;(2) With multiple receiving antennas, the attacker utilizes the ratio of CSIs between different antennas to wipe off the filter effect. Once the obfuscation is removed, SnoopFi uses a few-shot learning technique for precise sensing of user localization with constrained training samples. The experimental results show that SnoopFi achieves localization accuracies of 91.79% and 92.05% under the two strategies, respectively, with an average of only 18 samples per class.

关键词： Wi-Fi sensing Privacy concerns Channel obfuscation Unauthorized sensing wireless security

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Covert Rate Optimization of Millimeter Wave Full-Duplex communications

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IEEE TRANSACTIONS ON wireless communicationS 2022年第5期21卷 2844-2861页

作者： Wang, Chao Li, Zan Ng, Derrick Wing Kwan Xidian Univ Integrated Serv Networks Lab Xian 710071 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Networ Nanjing 210003 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

In this paper, we consider the problem of full-duplex covert millimeter wave (mmWave) communications, where a mmWave transmitter (Alice) sends information signals to its intended receiver (Bob) covertly in the presence of a watchful warden (Willie). For covering the presence of Alice, Bob operates in the full-duplex mode and generates jamming signals with a time-varying power. We investigate the covert rate optimization for both the single data stream case and the multiple data streams case under the constraints of the detection error probability at Willie. Specifically, for the single data stream case, we analytically characterize the minimum detection error probability at Willie and establish a framework for optimizing the analog beamforming, transmit power, and analog jamming jointly. As for the case of multiple data streams, we derive a tractable lower bound of the minimum detection error probability at Willie and formulate a joint optimization of the hybrid precoder and analog jamming design problem for the maximization of the achievable covert rate. Although the joint design problem is nonconvex, we adopt the penalty decomposition technique to handle the effect of the coupling between the analog precoder and digital precoder paving the way for the development of an iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we show that our proposed joint design algorithm can be adapted to handle the multi-antenna Willie scenario and simulation results show that our proposed joint design algorithms can achieve significantly better performance as compared with some benchmark schemes.

关键词： wireless communication Jamming Radio frequency Optimization Millimeter wave communication Massive MIMO Error probability Millimeter wave communication full duplex covert rate analog beamforming hybrid precoding

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基于马尔可夫决策过程的边缘云业务迁移算法

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北京航空航天大学学报 2024年第6期50卷 1931-1939页

作者：马安华潘甦南京邮电大学宽带无线通信与传感网技术教育部重点实验室南京210003

在移动互联网中,当用户位置发生改变时,业务迁移可用来提升服务质量(QoS)。基于此,提出一种基于马尔可夫决策过程的边缘云业务迁移算法。与对比算法相比,所提算法考虑了不同业务类型对QoS的差异化需求,并全面考虑了业务迁移过程中的收... 详细信息

在移动互联网中,当用户位置发生改变时,业务迁移可用来提升服务质量(QoS)。基于此,提出一种基于马尔可夫决策过程的边缘云业务迁移算法。与对比算法相比,所提算法考虑了不同业务类型对QoS的差异化需求,并全面考虑了业务迁移过程中的收益及开销。所提算法将业务分为实时和非实时2类,将终端的业务运行状态和与服务器的距离作为状态空间,并基于与业务体验紧密相关的可用速率和时延2个QoS指标构建收益函数,同时将业务迁移过程中的系统资源消耗作为迁移开销,通过最大化全局收益来获取最优迁移策略。通过与对比算法的模拟比较,所提算法在多种场景下都有更高的全局收益。

关键词：业务迁移马尔可夫决策过程服务质量迁移开销业务类型

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Auto-CsiNet: Scenario-Customized Automatic Neural network Architecture Generation for Massive MIMO CSI Feedback

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IEEE TRANSACTIONS ON wireless communicationS 2024年第10期23卷 14759-14775页

作者： Li, Xiangyi Guo, Jiajia Wen, Chao-Kai Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

Deep learning has brought about a revolution in the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process, and manual design can be prohibitively expensive for customized NNs tailored to different scenarios. To tackle this challenge, this paper proposes the use of neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures. By employing automated machine learning and gradient-descent-based NAS, an efficient and cost-effective architecture design process is achieved, requiring less expert experience and design time, thus lowering the design threshold. The proposed approach leverages implicit scene knowledge and integrates it into the scenario customization process in a data-driven manner, fully exploiting the potential of deep learning in a given scenario. To address the issue of excessive search, early stopping and elastic selection mechanisms are employed, further enhancing the proposed scheme. The experimental results demonstrate that the generated architecture, known as Auto-CsiNet, outperforms manually-designed models in terms of reconstruction performance (achieving approximately 14% improvement) and complexity (reducing by approximately 50%), highlighting the effectiveness of the NAS-based automatic scheme. Furthermore, the paper analyzes the impact of the scenario on the NN architecture and capacity.

关键词： Massive MIMO CSI feedback deep learning neural network architecture search Massive MIMO CSI feedback deep learning neural network architecture search

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Radar-aided reconfigurable intelligent surface for vehicular wireless communication

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OPTICS EXPRESS 2025年第5期33卷 11996-12011页

作者： Wang, Kun Tian, Yuze Yan, Tai Wang, Bo Shi, Xintong Xiong, Jie Yu, Wen Jin, Jing Lin, Hai Tang, Wenxuan Cent China Normal Univ Coll Phys Sci & Technol Wuhan 430079 Peoples R China South China Univ Technol Sch Future Technol Guangzhou 510641 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Southeast Univ Inst Electromagnet Space Nanjing 210096 Peoples R China

In the intricate traffic environment of modern cities, challenges like insufficient wireless signal coverage and obstructed propagation links between base stations (BS) and vehicles are prevalent. Meanwhile, reconfigurable intelligent surface (RIS) offers what we believe to be a novel solution relying on its robust real-time control over electromagnetic (EM) waves, providing a new approach to achieving intelligent transportation and vehicle connectivity. In this paper, we propose a radar-aided intelligent wireless communication system for dynamic beam tracking. The system leverages frequency-modulated continuous wave (FMCW) radar to detect the location information of the surrounding vehicles, a pre-trained artificial neural network (ANN) combined with control boards to generate the voltage coding sequence of the desired deflection direction, and the dual-polarized RIS to realize intelligent beam tracking and wireless communication. We design a 3-bit dual-polarized RIS, consisting of 256 (16 x 16) independently controllable units, running at 3.5 GHz, and develop a high-speed control board to ensure real-time refresh of the voltage coding sequence. Comparative experiments are conducted in three different environments. The experiments consistently demonstrate that, with the assistance of the radar, the RIS can achieve dynamic beam tracking without manual intervention. The system can effectively improve wireless signal strength and communication stability, providing a new viable approach for the development of intelligent vehicular networking. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Deep learning Field programmable gate arrays Machine vision Neural networks Phase shift Visible light communications

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Analysis and Prediction of Mobile Industrial Internet of Things (IIoT) communications Based on FL-GLP-Net

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IEEE INTERNET OF THINGS JOURNAL 2024年第12期11卷 21574-21584页

作者： Xu, Lingwei Cao, Shubo Li, Xingwang Gulliver, T. Aaron Qingdao Univ Sci & Technol Dept Informat Sci & Technol Qingdao 266061 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Anhui Prov Key Lab Adv Numer Control & Servo Tech Dept Mech & Automot Engn Wuhu 241000 Peoples R China Guangxi Minzu Univ Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Henan Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada

The number of mobile users and applications is rising quickly due to the deployment of fifth-generation (5G) communication technology. The prevalence of smart devices and Internet of Things (IoT) services has made this technology essential to manage the resulting volume of data. However, the increase in mobile communications raises security concerns considering the open and dynamic nature of the Industrial IoT (IIoT) environment. An important research problem is how to exploit the characteristics of wireless channels for safe and reliable information transmission. Therefore, a mobile communication performance analysis and prediction algorithm based on FL-GLP-Net is proposed. First, a mobile security communication system model based on N-Nakagami channels is presented. Then, the nonzero secrecy capacity probability (NSCP) is studied and an exact expression is derived. XGBoost is used to choose the best features based on model performance. A real-time mobile security performance prediction model based on FL-GLP-Net is designed for real-time NSCP prediction. Federated learning (FL), graph attention network (GAT), long short-term memory (LSTM), and pyramid visual converter (PVT) modules are integrated to obtain a model that can deal with the diverse signal features in mobile communication systems. Results are presented to show that the proposed method outperforms other NSCP prediction algorithms. In particular, the mean-squared error (MSE) of FL-GLP-Net is 78% better than that of FL-ShuffleNetV2.

关键词： Federated learning (FL) graph attention network (GAT) intelligent prediction long short-term memory (LSTM) mobile secure communication

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An Improved Data Fusion Algorithm Based on Cluster Head Election and Grey Prediction

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IEEE ACCESS 2024年 12卷 22746-22758页

作者： Wang, Jun Wang, Ning Sun, Bingnan Cao, Kerang Jung, Hoekyung El-Meligy, Mohammed A. Sharaf, Mohamed Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Coll Engn Dept Comp Engn Shenyang 110142 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

In traditional wireless sensor network routing protocols, data collected through timed interval sensing tends to have high temporal redundancy, which leads to unnecessary energy drain. To alleviate this problem and enable sensor networks to save energy to some extent, a practical solution is to utilize prediction-based data fusion methods. To this end, this paper first proposes a Low Energy Adaptive Clustering Hierarchy-Energy-Kopt-N algorithm, an optimization algorithm explicitly designed to address the cluster-head election phase of the Low Energy Adaptive Clustering Hierarchy protocol. Then, a data collection model using data prediction techniques - the Grey Data Prediction Model is formatted. Combining these improvements, a new data fusion algorithm that relies on data prediction, Grey-Clusters-Leach (GCL), is proposed. Simulation experiments demonstrate that the network energy drain of the GCL algorithm is reduced by 18%, 35%, 21.5% and 20%, and the network operation critical period life is extended by 3%, 35%, 22%, and 5% compared to the EQDC LEACH, LEACH-E, and SEP algorithms, respectively. GCL can effectively manage the size and number of clusters and reduce the number of packet transmissions by 20%.

关键词： Prediction algorithms Data models Adaptation models wireless sensor networks Clustering algorithms Predictive models Integrated circuit modeling wireless sensor network cluster head election grey model data fusion

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