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IEEE Conference on Wireless Communications and networking

作者： Wenzheng Wang Renchao Xie Qinqin Tang Tao Huang Tianjiao Chen Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China China Mobile Research Institute Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

关键词： Computational modeling Software algorithms Distribution networks Routing Software Complexity theory Reliability Software defined networking Low latency communication Edge computing

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Semi-Asynchronous Federated Split Learning for Computing-Limited Devices in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Ao, Huiqing Tian, Hui Ni, Wanli Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science Singapore

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness. © 2002-2012 IEEE.

关键词： Federated learning

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Construction and Count of 1-Resilient Rotation Symmetric Boolean Functions on pr Variables

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Chinese Journal of Electronics 2025年第4期23卷 816-820页

作者： Jiao Du Shanqi Pang Qiaoyan Wen Xin Liao College of Mathematics and Information Science Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Information Science and Engineering Hunan University Changsha China

Construction and count of 1-resilient Rotation symmetric Boolean functions (RSBFs) on $p^{r}$ variables are demonstrated. It is proved that constructions of 1-resilient RSBFs on $p^{r}$ variables are equivalent to solving an equation system. An accurate enumeration formula of all 1-resilient RSBFs on $p^{r}$ variables is also proposed. Some examples are given, and the exact numbers of 1-resilient RSBFs on 8 and 9 variables are obtained respectively.

关键词： Sufficient conditions Boolean functions Accuracy Education Medical services NP-complete problem Cryptography

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On the Design of NOMA-Based Integrated Sensing and Communications (ISAC) in Near-Field Extremely Large-Scale MIMO Systems

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IEEE Internet of Things Journal 2025年

作者： Sun, Like Zhao, Zhongyuan Jia, Chao Quek, Tony Q. S. Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering Beijing100876 China Singapore University of Technology and Design 487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

Integrated sensing and communications (ISAC) has been widely applied in Internet of Things (IoT) networks for its capability to simultaneously support high-performance communication and sensing, and non-orthogonal multiple access (NOMA) is introduced to further improve spectral efficiency and connection density. However, with the deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) and high-frequency (HF) technologies, the near-field (NF) paradigm replaces the conventional far-field (FF) paradigm and becomes dominant, necessitating a reassessment of NOMA-based ISAC system performance in the NF region. To address this, a novel NOMA-based ISAC scheme in NF XL-MIMO systems is proposed in this paper, wherein a multi-beam design based on subarray partitioning is employed to realize a communication-and-sensing coexistence ISAC system, while the additional distance-based degree of freedom (DoF) provided by the unique NF beamfocusing is utilized to improve NOMA performance gains. To balance the optimal performance tradeoff between communication and sensing, an optimization problem for joint device scheduling, subarray partitioning, and power allocation is formulated to maximize the ISAC joint rate under various constraints. Based on alternating optimization (AO) and fractional programming (FP) techniques, an efficient joint optimization algorithm is developed to solve the complex non-convex problem with coupled variables. In particular, the original problem is decoupled into three subproblems and solved by applying the linearization of 0-1 polynomial programming, Lagrangian dual reformulation, and quadratic transform techniques. Numerical results validate that our proposed NOMA-based ISAC scheme and joint optimization algorithm significantly enhance the ISAC joint rate performance in NF XL-MIMO systems for IoT networks. © 2014 IEEE.

关键词： Quadratic programming

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Robust Transmission Design for Covert Satellite Communication Systems With Dual-CSI Uncertainty

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IEEE Internet of Things Journal 2025年

作者： Jia, Huaiqi Wang, Ying Wu, Wen Yuan, Jun Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Frontier Research Center Shenzhen518000 China Joint Laboratory of Technology and Applications on Frequency Orbital Space Information Networks China Satellite Network System Research Institute Co Ltd Beijing100070 China

In this paper, we investigate a novel covert transmission scheme for satellite communication systems. Specifically, the satellite employs a rate-splitting multiple access technique to covertly transmit messages to multiple users and simultaneously transmit jamming signals, thereby enhancing the covert rate and robustness while avoiding detection by a warden. Due to the long propagation delay and lack of the warden's precise location information, acquiring perfect channel state information (CSI) between the satellite, covert users, and the warden is difficult. To this end, we establish a dual-CSI uncertainty model, which incorporates phase and norm-bounded uncertainty for the covert and wiretap channels to characterize the actual CSI. In addition, we formulate a stochastic optimization problem with the objective of maximizing the minimum covert rate while adhering to covert communication constraints. The optimization problem is non-convex and difficult to solve directly due to the dual-CSI uncertainty and the coupled nature of the variables. To solve the problem, we reformulate the original problem into a series of convex optimization problems by utilizing semidefinite relaxation, fractional programming, and the S-procedure methods. Then, we propose a robust Common Rate Allocation and Beamforming (CRAB) algorithm to obtain near-optimal solutions for the beamforming vectors and common rate allocation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms baseline schemes in terms of both covert rate and robustness. © 2014 IEEE.

关键词： Satellite communication systems

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MACS-BNet: A Stealthy Multi-Constraint Adversarial Backdoor Network Against Compressed Learning

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IEEE Internet of Things Journal 2025年

作者： Zhu, Haotian Wu, Wei Peng, Haipeng Zhao, Dawei Beijing University of Posts and Telecommunications Information Security Center State Key Laboratory of Networking and Switching Technology Beijing100876 China Shandong Normal University School of Information Science and Engineering Jinan250358 China Jinan250353 China

Deep learning-based compressed sensing techniques have exhibited exceptional prowess in signal reconstruction and data-sharing applications, particularly within the realm of IoT sensor data processing. However, existing methods overlook a critical security vulnerability: the susceptibility of compressed sensing techniques to backdoor attacks during the reconstruction phase, which could pose severe security risks to downstream applications. This study pioneers an investigation into the feasibility of backdoor injection during the reconstruction phase, presenting the stealthy multi-constraint adversarial backdoor network against compressed learning (MACS-BNet) and substantiating its efficacy in subverting downstream classification tasks. MACS-BNet synergistically incorporates detailed sensing enhancement, fortified by local information relative positional encoding (LiRPE), to elevate image reconstruction fidelity. Concurrently, it employs a multi-constrained adversarial optimization that integrates sparsity, amplitude regulation, and spatial smoothness constraints, achieving an optimal trade-off between perturbation imperceptibility and attack efficacy. Consequently, victim models are subtly manipulated to yield outputs consistent with the attacker’s objectives. Extensive empirical evaluations reveal that MACS-BNet consistently surpasses seven cutting-edge attack methodologies across attack success rate, clean sample classification accuracy, and stealthiness under both all-to-one and all-to-all attack paradigms. Specifically, MACS-BNet attains an unparalleled clean classification accuracy of 99.52% and an attack success rate of 99.43% in the all-to-one mode, while simultaneously ensuring high-quality image reconstruction. Furthermore, MACS-BNet exhibits formidable resistance against detection by seven state-of-the-art defense mechanisms, underscoring its superior stealth and robustness. © 2014 IEEE.

关键词： Network security

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Joint Service Placement and Model Partitioning for Accelerating DNN Inference in Edge Intelligence Empowered Vehicle Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Zhang, Wenzhao Han, Shujun Xu, Xiaodong Zhang, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies Beijing China Peng Cheng Laboratory Department of Broadband Communication Guangdong Shenzhen China

This paper investigates the joint service placement and model partitioning problem for accelerating DNN inference in edge intelligence empowered vehicle networks, considering the vehicle mobility and the caching capability of Road Side Unit (RSU). To avoid the degradation of DNN inference performance due to the cold start during the vehicle's service node switching, we propose three different service placement solutions, including processing locally, directly selecting one RSU as server node for collaborative offloading, and selecting one RSU as relay to maintain the connection with original server node. We model the RSU network as an undirected weighted graph and formulate an optimization problem to minimize the DNN inference latency by jointly optimizing the DNN service placement and DNN model partitioning. To solve this problem, we propose a Graph Neural Network Reinforcement learning-based service Placement and model Partitioning algorithm (GRPP). Initially, we extract the feature embedding of RSU nodes and obtain the DNN service placement solution based on Graph Neural Network (GNN). Furthermore, we design a Multilayer Perceptron (MLP) to generate the DNN model partitioning point. Specifically, the parameters of GNN and MLP are learned through an actor-critic based reinforcement learning structure. Finally, we generate the simulation model via traffic simulator Simulation for Urban MObility (SUMO) and give a comprehensive performance evaluation. Simulation results indicate that our proposed scheme can decrease the DNN inference latency by up to 64.51% compared with the benchmark. © 1967-2012 IEEE.

关键词： edge intelligence end-edge collaborative paradigm model partitioning Service placement vehicle networks

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SNR-adaptive deep joint source-channel coding scheme for image semantic transmission with convolutional block attention module

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The Journal of China Universities of Posts and Telecommunications 2024年第1期31卷 1-11页

作者： Yang Yujia Liu Yiming Zhang Wenjia Zhang Zhi State Key Laboratory of Networking and Switch Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

With the development of deep learning(DL),joint source-channel coding(JSCC)solutions for end-to-end transmission have gained a lot of *** deep JSCC schemes support dynamically adjusting the rate according to different channel conditions during transmission,enhancing robustness in dynamic wireless ***,most of the existing adaptive JSCC schemes only consider different channel conditions,ignoring the different feature importance in the image processing and *** uniform compression of different features in the image may result in the compromise of critical image details,particularly in low signal-to-noise ratio(SNR)*** address the above issues,in this paper,a dual attention mechanism is introduced and an SNR-adaptive deep JSCC mechanism with a convolutional block attention module(CBAM)is proposed,in which matrix operations are applied to features in spatial and channel dimensions *** proposed solution concatenates the pooling feature with the SNR level and passes it sequentially through the channel attention network and spatial attention network to obtain the importance evaluation *** show that the proposed solution outperforms other baseline schemes in terms of peak SNR(PSNR)and structural similarity(SSIM),particularly in low SNR scenarios or when dealing with complex image content.

关键词： semantic communication joint source-channel coding image transmission

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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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A Hybrid Federated Learning Framework for Task-Oriented Semantic Communication

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IEEE Internet of Things Journal 2025年

作者： Sun, Haofeng Ni, Wanli Tian, Hui Zheng, Jingheng Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science 117583 Singapore

In existing deep learning-based semantic communication systems, centralized training of semantic models brings a risk of privacy leakage, whereas distributed training imposes a huge computational burden on user equipments (UEs). To address these challenges, we propose a hybrid federated learning (Hybrid-FL) framework to alleviate the computational burden on UEs while protecting the user privacy. Specifically, each UE uploads local gradients and semantic symbols to the base station for the collaborative training of global and local semantic models. Furthermore, we propose a joint communication and computation scheme for supporting the model aggregation and semantics transmission. To gain deep insights, we expose the joint impact of communication and computation on the convergence behavior of Hybrid-FL by deriving an upper bound. Then, we formulate a mixed-integer non-linear programming problem to improve the convergence performance of Hybrid-FL, which is then effectively solved by using our proposed algorithm that developed based on alternating and matching theory. Experimental results demonstrate that Hybrid-FL outperforms conventional FL by achieving a 20% accuracy gain and a 80% latency reduction. © 2014 IEEE.

关键词： Federated learning

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