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Online Energy Efficient Multi-modal Probabilistic Semantic communication

IEEE Internet of Things Journal 2025年

作者： Dai, Jianxin Li, Jingjing Zhao, Zhouxiang Yang, Zhaohui Ye, Jianglin Yang, Qianqian Huang, Chongwen Zhang, Zhaoyang Nanjing University of Posts and Telecommunications School of Science Nanjing 210023 China Southeast University National Mobile Communications Research Laboratory Nanjing 210096 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou 310058 China Zhejiang Provincial Key Laboratory of information Processing Communication and Networking (IPCAN) Hangzhou 310007 China

In this paper, we investigate an uplink multi-modal probabilistic semantic communication (PSCom) system based on probability graph in the satellite scenario. The system consists of both: semantic computation and traditional communication. Firstly, at the user end, the transmitted data is compressed based on the probabilistic graph. Then, the compressed data is transmitted to the satellite, which uses the same probabilistic graph to recover the received data. In the considered model, this paper addresses an optimization problem for multi-modal multi-user semantic communication across multiple time slots. An optimization problems formulated aiming to minimize the total energy consumption of the PSCom system, with satisfying the transmission time, transmission power, transmission bandwidth, local computation frequency, and transmission data requirements. To solve this problem, the Lyapunov drift-plus-penalty function based on online optimization is first used to transform the multi-slot problem into a stochastic single-slot problem, thereby converting the optimization problem into a trade-off between system energy consumption and queue length. Subsequently, an alternating algorithm is proposed to iteratively optimize, semantic compression rate, local computation frequency, transmission bandwidth, transmission power, and time allocation variables. Finally, simulation experiments demonstrates the effectiveness of the proposed algorithm. © 2014 IEEE.

关键词： knowledge base multi-modal transmission probability graph Semantic communications

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Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface Under Imperfect CSI

Safeguarding NOMA Networks via Reconfigurable Dual-Functiona...

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作者： Wang, Wen Ni, Wanli Tian, Hui Yang, Zhaohui Huang, Chongwen Wong, Kai-Kit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Zhejiang University College of Information Science and Electronic Engineering Zhejiang Hangzhou310027 China Zhejiang Provincial Key Lab of Information Processing Hangzhou Zhejiang310007 China Hangzhou Zhejiang310027 China University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom

This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks. In the presence of eavesdroppers, the downlink communication from the base station to the legitimate users is safeguarded by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), where three practical operating protocols, namely energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The joint optimization of power allocation, active and passive beamforming is investigated to maximize the secrecy energy efficiency (SEE), taking into account the imperfect channel state information (CSI) of all channels. For ES, by approximating the semi-infinite constraints with the S-procedure and general sign-definiteness, the problem is solved by an alternating optimization framework. Besides, the proposed algorithm is extended to the MS protocol by solving a mixed-integer non-convex problem. While for TS, a two-layer iterative method is proposed. Simulation results show that: 1) The proposed STAR-RIS assisted NOMA networks are able to provide up to 33.6% higher SEE than conventional RIS counterparts;2) TS and ES protocols are generally preferable for low and high power domain, respectively;3) The accuracy of CSI estimation and the bit resolution power consumption are crucial to reap the SEE benefits offered by STAR-RIS. © 2007-2012 IEEE.

关键词： Energy efficiency

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Time-delay Robust Safety Control of Connected Vehicles under Stochastic DoS Attack

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IEEE Internet of Things Journal 2025年

作者： Song, Xiulan Liu, Xueyang Shan, Hangguan Lu, Rongxing Zhejiang University of Technology College of Information Engineering Hangzhou310023 China University of Birmingham School of Engineering BirminghamB15 2TT United Kingdom Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonE3B 5A3 Canada

Connected vehicles utilize vehicle-to-vehicle (V2V) wireless communication networks to gather vital information for platoon tracking control, not only enhancing road traffic efficiency but also preventing collisions. This paper introduces a robust safety platooning control method for connected vehicles, which addresses safety challenges in heterogeneous vehicle platoon systems, including time delays, dynamic uncertainties, and stochastic denial-of-service (DoS) attacks. Specifically, we present an uncertain longitudinal dynamic model for heterogeneous vehicles to account for variable driving conditions. Then, a robust safety platooning controller, based on linear matrix inequality (LMI) and Markov jump system theory, is designed. This controller considers communication delays to ensure vehicle-following stability under dynamic uncertainties and stochastic DoS attacks. Finally, the effectiveness of the proposed algorithm is validated through co-simulations using MatLab and CarSim, as well as actual vehicle experiments, demonstrating its capability to mitigate the impact of communication delays, dynamic uncertainties, and stochastic DoS attacks. © 2014 IEEE.

关键词： Markov processes

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GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature

arXiv

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arXiv 2022年

作者： Liu, Biyang Yu, Huimin Qi, Guodong College of Information Science and Electronic Engineering Zhejiang University China ZJU-League Research & Development Center China State Key Lab of CAD&CG Zhejiang University China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking China

Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://***/SpadeLiu/Graft-PSMNet. © 2022, CC BY.

关键词： Grafting (chemical)

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SCAN-BEST: Efficient Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees

arXiv

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arXiv 2025年

作者： Deng, Weicao Shi, Binpu Li, Min Simeone, Osvaldo College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Zhejiang University Hangzhou310027 China Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

As millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems continue to incorporate larger antenna arrays, the range of near-field propagation expands, making it more likely for users close to the transmitter to fall within the near-field regime. Traditional far-field beam training methods are no longer effective in this context. Additionally, near-field beam training presents challenges, since the training codebook must account for both angular and distance dimensions, leading to large codebook sizes. To reduce the in-band training overhead, we propose the Sub-6G Channel-Aided Near-field BEam SelecTion (SCAN-BEST) framework, which is motivated by the spatial-temporal congruence between sub-6 GHz (sub-6G) and mmWave channels. SCAN-BEST utilizes preprocessed sub-6G channel estimates as input, and employs a convolutional neural network (CNN) to predict the probability of each beam being optimal within the near-field beam training codebook. Given the prediction uncertainty arising from the variance between sub-6G and mmWave channels, we introduce a conformal risk control (CRC)-based module that generates a set of beam candidates for further limited in-band training, enabling the final beam selection to formally meet user-defined target coverage rate. Numerical results confirm the thereoretical properties of SCAN-BEST in terms of the achieved coverage rate of the beam candidates and various metrics. Moreover, SCAN-BEST enjoys good scalability and robustness to various sub-6G system configurations, including to the sizes of calibration datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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MTL-SRN: Multi-task Learning-based Signal Recognition Network

MTL-SRN: Multi-task Learning-based Signal Recognition Networ...

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IEEE Conference on Global communications (GLOBECOM)

作者： Pengxu Wang Xingjian Zhang Yuan Ma Hao Zhu Jian Jiao Qinyu Zhang Department of Electronics and Information Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing and the College of Electronic and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Wideband signal recognition is a crucial task in the cognitive wireless communication, involving accurate classification of different signal types, modulation types, center frequencies, etc. However, most conventional approaches treat the recognition of different parameters as multiple independent tasks, and often face performance bottlenecks due to the complexity and diversity of wideband signals. To overcome these challenges, we propose a multi-task learning (MTL) network that integrates multiple tasks of signal recognition into an end-to-end model to accomplish spectrum sensing, modulation recognition, and signal classification simultaneously. By employing a shared feature extraction network and a multi-task classification header, the proposed framework effectively captures the correlations and shared information among different tasks, thereby enhancing overall recognition performance. To validate the effectiveness of the proposed scheme, we compare its performance with other state-of-the-art recognition and classification networks. Experimental results demonstrate the significant performance of the proposed MTL network in spectrum sensing, modulation recognition, and signal classification tasks.

关键词： Wireless communication Correlation Accuracy Frequency modulation Face recognition Pattern classification Multitasking Sensors Global communication Wideband

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Frame-Level Video Caching and Transmission Scheduling via Stochastic Learning

Frame-Level Video Caching and Transmission Scheduling via St...

引用

2021 IEEE Global communications Conference (GLOBECOM)

作者： Ziwei Zheng Wei Wang Hangguan Shan Zhaoyang Zhang Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781728181059

To meet the ever-increasing demand for mobile video services, one of the effective solutions is caching some popular videos in edge nodes. In this paper, we propose an online stochastic learning algorithm with two time scales for joint caching and transmission optimization in the video frame level. To overcome the drift distortion caused by the dependency among video frames, the transmission process is formulated as an infinite horizon Markov decision process (MDP). We derive the equivalent Bellman equation and design the online value iteration algorithm via stochastic approximation for transmission. Due to the lack of the expression between the system performance and the caching policy, we design a gradient-free stochastic optimization algorithm to update the caching policy. Finally, simulation results show that our proposed algorithm achieves better performance than conventional caching algorithms.

关键词： Simulation System performance Markov processes Approximation algorithms Scheduling Mathematical models Mobile video

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Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity

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IEEE Transactions on Mobile Computing 2025年

作者： Zuo, Shiyuan Yan, Xingrun Fan, Rongfei Hu, Han Shan, Hangguan Quek, Tony Q. S. Zhao, Puning Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Communication and Networking Zhejiang Provincial Key Laboratory of Information Processing Hangzhou China Singapore University of Technology and Design 487372 Singapore Yonsei University Yonsei Frontier lab Korea Republic of Sun Yat-Sen University School of Cyber Science and Technology Shenzhen518107 China

This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), which uses the geometric median for aggregation and allows flexible round number for local updates. Unlike most existing resilient approaches, which base their convergence analysis on strongly-convex loss functions or homogeneously distributed datasets, this work conducts convergence analysis for both strongly-convex and non-convex loss functions over heterogeneous datasets. The theoretical analysis indicates that as long as the fraction of the data from malicious users is less than half, RAGA can achieve convergence at a rate of O(1/T2/3-δ) for non-convex loss functions, where T is the iteration number and δ ∈ (0, 2/3). For strongly-convex loss functions, the convergence rate is linear. Furthermore, the stationary point or global optimal solution is shown to be attainable as data heterogeneity diminishes. Experimental results validate the robustness of RAGA against Byzantine attacks and demonstrate its superior convergence performance compared to baselines under varying intensities of Byzantine attacks on heterogeneous datasets. © 2002-2012 IEEE.

关键词： Geometry

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Geometry-based non-line-of-sight error mitigation and localization in wireless communications

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Science China(information Sciences) 2019年第10期62卷 149-163页

作者： Jingyu HUA Yejia YIN Anding WANG Yu ZHANG Weidang LU School of Information and Electronic Engineering Zhejiang Gongshang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Engineering Zhejiang University of Technology

Recently, positioning services have received considerable attention. The primary source of the positioning error is non-line-of-sight(NLOS) propagation. To address this problem, we propose a novel NLOS mitigation scheme, in which the geometric relationship between a base station and a mobile station is *** makes it possible to identify range measurements corrupted by NLOS errors, and the mobile station can then estimate its position through line-of-sight(LOS) measurements. Moreover, the threshold of the NLOS detector is derived via a hybrid method using both the analytical derivation and computer simulation, which significantly reduces the difficulty of identifying thresholds. After identifying the NLOS measurements, a two-step weighted-least-squares algorithm is used to obtain the localization, in which both range and angle measurements are considered. The simulation results reveal that the proposed algorithm yields a high identification probability of NLOS measurements, which results in improved localization performance.

关键词： wireless localization non-line-of-sight error geometry cellular network residual

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Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface under Imperfect CSI

arXiv

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arXiv 2022年

作者： Wang, Wen Ni, Wanli Tian, Hui Yang, Zhaohui Huang, Chongwen Wong, Kai-Kit The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China The Department of Electronic and Electrical Engineering University College London London United Kingdom Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou China

关键词： Energy efficiency

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