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IEEE Conference on Wireless Communications and networking

作者： Jun Wang Kailin Tan Sixian Wang Xiaoqi Qin Zhenyu Liu Jincheng Dai Key Laboratory of Universal Wireless Communications Ministry of Education State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Institute for Communication Systems University of Surrey United Kingdom

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We propose “MaskDSC”, a novel system designed to facilitate robust visual data transmission over unreliable wireless channels. MaskDSC effectively balances compression efficiency and transmission resilience by leveraging contextual modeling within the semantic latent space, complemented by unequal error protection mechanism at the physical layer, ensuring compatibility with existing digital communication systems. The novelty of our approach lies in a dual-functional masked Transformer architecture that exploits causal-order contextual dependencies among visual tokens. This architecture not only enhances compression efficiency through improved contextual entropy modeling but also provides robust error concealment capabilities to address diverse transmission error patterns inherent in volatile wireless channels. Our experimental evaluations conducted on image datasets demonstrate that MaskDSC outperforms state-of-the-art transmission systems, especially in terms of efficiency and resilience under dynamic wireless channel conditions.

关键词： Wireless communication Visualization Image coding Time-varying channels Transformers Digital communication Entropy Protection Resilience Context modeling

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NestFL: Enhancing Federated Learning Through Nested Multi-Capacity Model Pruning in Heterogeneous Edge Computing

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IEEE Internet of Things Journal 2025年

作者： Zhou, Xiaomao Hu, Yujiao Jia, Qingmin Xie, Renchao Purple Mountain Laboratories Future Network Research Center Nanjing211111 China Beijing University of Posts and Telecommunications State Key Laboratory of networking and Switching Technology Beijing100876 China

Federated learning (FL) has emerged as a pivotal approach for edge-based distributed machine learning, yet it faces significant challenges due to the constrained capacities and heterogeneity of edge devices, including non-IID data distribution, communication constraints, and learning inefficiencies. Furthermore, a one-fits-all global model often fails to perform optimally across diverse participating devices. In this paper, we present NestFL, an efficient FL framework for edge computing that can jointly improve the training efficiency and achieve personalization. Specifically, NestFL innovates by incorporating distributed model pruning, creating a hierarchy of structured-sparse subnetworks tailored to the unique resource profiles of client devices. These subnetworks are integrated into a nested global model, ensuring parameter sharing without increasing the parameter space, thereby significantly reducing computational and communication burdens. Meanwhile, it implements a cross-training mechanism, allowing clients to train on a broader dataset and maintain consistent decision boundaries. Furthermore, a weighted aggregation mechanism is designed to improve training performance and maximally preserve personalization. Experimental results in different applications demonstrate the superiority of NestFL over the baseline approaches in terms of model accuracy, convergence speed, and personalization preservation. © 2014 IEEE.

关键词： Federated learning

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Joint Beamforming Design for Multi-Functional RIS-Aided Over-the-Air Computation

Joint Beamforming Design for Multi-Functional RIS-Aided Over...

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

作者： Xinran Zhang Hui Tian Wanli Ni State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

To facilitate fast data aggregation in Internet of Things, over-the-air computation (AirComp) is a communication-efficient enabler by virtue of its high spectrum efficiency and low transmission latency. However, traditional AirComp faces challenges such as signal misalignment, imperfect channel state information (CSI), and noisy fading channels. In this paper, we employ a multi-functional reconfigurable intelligent surface (MF-RIS) to alleviate mean square error (MSE) of AirComp through a joint design of transceiver beamforming and MF-RIS coefficients, but it necessitates solving a mixed-integer nonlinear programming problem. To tackle this issue, we propose an alternating optimization algorithm based on the semidefinite relaxation approach and difference-of-convex programming. Numerical results underscore the performance gains achieved by the proposed algorithm, as well as the remarkable proficiency of the MF-RIS in suppressing MSE under imperfect CSI.

关键词： Uncertainty Array signal processing Wireless networks Signal processing algorithms Channel estimation Programming Data aggregation Transceivers Speech processing Surface treatment

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VAAC-IM: Motion-aware Viewing Area Adaptive Control in Immersive Media Transmission

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Xiao, Han Xu, Changqiao Fang, Chuxing Wang, Wendong Yang, Shujie Yuan, Zhenhui Grieco, Luigi Alfredo Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology P.R Beijing100876 China University of Warwick United Kingdom Bari70125 Italy

Viewport in immersive media corresponds to the field of view (FoV), playing a critical role in both data transmission volume and user experience. However, instantaneous and highly dynamic interactions often conflict with segment-based transmission modes, resulting in substantial redundant data transmission and wastage of valuable resources. In this paper, we analyze data from an open-source dataset and our self-collected records to investigate the interactive characteristics of viewers in immersive scenes, including focus time, viewing area scope, movement direction, and tile access probability. Based on empirical statistical inference, we innovatively introduce the concept of an irregular, expandable, and directional extended field of view (EoV) to describe the dynamically variable area mimicking human visual motion. Furthermore, we propose a motion-aware tile-based adaptive control scheme for viewing areas, named VAAC-IM, designed to enable flexible transmission of immersive media. Specifically, we develope an FoV prediction model based on ConvLSTM, leveraging spatiotemporal features from historical viewing records to provide advanced predictions of visual motion preferences. Subsequently, we model the viewing area control process as a constrained submodular minimization problem, dynamically managing irregular EoV area using marginal effects. Finally, we perform a comprehensive validation. The results demonstrate that VAAC-IM significantly enhances performance in terms of reducing black edge coverage, minimizing data volume, lowering latency, and improving overall user experience. © 2025 IEEE. All rights reserved.

关键词： Adaptive control systems

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Efficient and Adaptive Human Pose Estimation on Resource-Constrained Computing Devices via Knowledge Distillation and Temporal Propagation

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IEEE Internet of Things Journal 2025年

Existing video-based human pose estimation methods commonly rely on large networks to localize body joints across all frames, achieving remarkable accuracy but imposing high memory and computational demands that limit their applications on resource-constrained devices. Moreover, most models lack the capability to accommodate dynamic changes in available resources, which can negatively impact the performance of parallel tasks. To address these issues, this paper proposes a novel yet effective framework for efficient and adaptive human pose estimation on resource-constrained devices. Specifically, the proposed approach adopts the knowledge distillation (KD) strategy to train a light-weight pose estimator network, which is capable of executing rapidly with low computational cost. To further increase the overall efficiency, it exploits the temporal coherence between successive video frames and explicitly propagates body joints from previous frames rather than naively extracting them using a pose estimator. Furthermore, a prediction-based mechanism is adopted to facilitate adaptive key-frame selection, dynamically determining the optimal number of keyframes, thus enhancing the overall efficiency and adaptability. Experiments on Penn Action, Sub-JHMDB, and real-world systems demonstrate that the proposed method achieves comparative accuracy, superior efficiency, and robust flexibility in dynamic scenarios. © 2025 IEEE.

关键词： Joints (anatomy)

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Multi-Agent Reinforcement Learning for Task Offloading in Crowd-Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Yao, Su Wang, Mu Ren, Ju Xia, Tianyu Wang, Weiqiang Xu, Ke Xu, Mingwei Zhang, Hongke China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Ant Group China Beijing Jiaotong University School of Electronic and Information Engineering China

The Crowd-edge (CE) computing paradigm facilitates the utilization of the computational resources through simultaneously relying the edge computing and the collaboration among various mobile devices (MDs). Most existing works, focusing on offloading tasks from device to edge servers by centralized solutions, are unable to distribute tasks to massive MDs in CE. Meanwhile, designing a decentralized task offloading solution enabling task subscribers to individually make offloading decisions can be challenging given the randomness of crowd resource provisioning and limited knowledge of global status variations. In this paper, we propose a decentralized crowd-edge task offloading solution that enables users to optimally offload tasks to the CE in a distributed manner. Specifically, we formulate the corresponding problem as a stochastic optimization with partially observable status. By observing network and process delays at the crowd side, we further reform the optimization forms and provide a novel approximation policy, enabling users to optimize their offloading strategy based on local observations without interaction with each other. We then solve this task offloading problem by developing a Mixed Multi-Agent Proxy Policy Optimization algorithm (mixed MAPPO). Extensive testing, including numerical and system-level simulations, was conducted to validate the performance of the proposed algorithm in terms of task delay (including the processing delay and transmission delay), load rate, and resource utilization. © 2025 IEEE.

关键词： Stochastic systems

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An Approximate Wave-Number Domain Expression for Near-Field XL-Array Channel

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IEEE Transactions on Vehicular technology 2025年

作者： Xing, Hongbo Zhang, Yuxiang Zhang, Jianhua Xu, Huixin Liu, Guangyi Wang, Qixing Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing100876 China China Mobile Research Institute Future Research Laboratory Beijing100053 China

As Extremely large-scale array (XL-array) technology advances and carrier frequency rises, the near-field effects in communication are intensifying. In near-field conditions, channels exhibit a diffusion phenomenon in the angular domain, existing research indicates that this phenomenon can be leveraged for efficient parameter estimation and beam training. However, the channel model in angular domain lacks closed-form analysis, making the time complexity of the corresponding algorithm high. To address this issue, this paper analyzes the near-field diffusion effect in the wave-number domain, where the wave-number domain can be viewed as the continuous form of the angular domain. A closed-form approximate wave-number domain expression is proposed, based on the Principle of Stationary Phase. Subsequently, we derive a simplified expression for the case where the user's distance is much larger than the array aperture, which is more concise. Subsequently, we verify the accuracy of the proposed approximate expression through simulations and demonstrate its effectiveness using a beam training example. Results indicate that the beam training scheme, improved by the wave-number domain approximation model, can effectively estimate near-field user parameters and perform beam training using far-field DFT codebooks. Moreover, its performance surpasses that of existing DFT codebook-based beam training methods. © 1967-2012 IEEE.

关键词： Channel estimation

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load switches

arXiv

引用

arXiv 2025年

作者： Zhang, Penghui Zhang, Hua Dai, Yuqi Zeng, Cheng Wang, Jingyu Liao, Jianxin National Mobile Communications Research Laboratory Southeast University Nanjing211111 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

In-band network telemetry (INT) is essential to network management due to its real-time visibility. However, because of the rapid increase in network devices and services, it has become crucial to have targeted access to detailed network information in a dynamic network environment. This paper proposes an intelligent network telemetry system called NTP-INT to obtain more fine-grained network information on high-load switches. Specifically, NTP-INT consists of three modules: network traffic prediction module, network pruning module, and probe path planning module. Firstly, the network traffic prediction module adopts a Multi-Temporal Graph Neural Network (MTGNN) to predict future network traffic and identify high-load switches. Then, we design the network pruning algorithm to generate a subnetwork covering all high-load switches to reduce the complexity of probe path planning. Finally, the probe path planning module uses an attention-mechanism-based deep reinforcement learning (DEL) model to plan efficient probe paths in the network slice. The experimental results demonstrate that NTP-INT can acquire more precise network information on high-load switches while decreasing the control overhead by 50%. Copyright © 2025, The Authors. All rights reserved.

关键词： Telemetering systems

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Enabling High-Reliable Routing for Non-Disjoint Redundant Paths in Time-Sensitive Networks

Enabling High-Reliable Routing for Non-Disjoint Redundant Pa...

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IEEE Conference on Wireless Communications and networking

作者： Kangzhe Zhao Shuo Wang Guoyu Peng Siyu Han Bingqing Wang Xinyue Liao Tao Huang State Key Laboratory of Networking and Switching Technology BUPT Beijing P.R. China Purple Mountain Laboratories Nanjing P.R. China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of critical applications like autonomous systems and remote healthcare, ultra-reliable networks have become indispensable. IEEE 802.1CB Frame Replication and Elimination for Reliability (FRER) protocol is prevalent in Time-Sensitive Networks (TSN). FRER utilizes multiple disjoint paths as redundant routes to ensure the reliability of time-sensitive streams. However, the multi-path in FRER may induce packet burst due to the different delays between paths, and the reliability of TSN may degrade when disjoint paths are absent. In this paper, we propose a delay duration difference model with a novel metric to evaluate the degree of packet burst when streams converge. We design a reliability calculation model that allows for more accurate reliability assessments in TSN with non-disjoint redundant paths. Moreover, we design a Burst and Junction-node Optimization Routing (BJOR) algorithm that stands apart from prior studies by uniquely addressing packet burst and the scenario of non-disjoint paths within FRER. Through evaluations against other state-of-the-art algorithms in TSN, our approach effectively reduces packet burst and significantly enhances the reliability of TSN across various topologies.

关键词： Measurement Network topology Medical services Routing Reliability engineering Routing protocols Topology Delays Streams Optimization

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