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arXiv 2025年

作者： Wang, Sixian Dai, Jincheng Qin, Xiaoqi Yang, Ke Niu, Kai Zhang, Ping Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs tend to be sensitive to packet losses, which are prevalent in real-time communications. In this paper, we investigate how to elevate the resilience ability of NICs to combat packet losses. We propose ResiComp, a pioneering neural image compression framework with feature-domain packet loss concealment (PLC). Motivated by the inherent consistency between generation and compression, we advocate merging the tasks of entropy modeling and PLC into a unified framework focused on latent space context modeling. To this end, we take inspiration from the impressive generative capabilities of large language models (LLMs), particularly the recent advances of masked visual token modeling (MVTM). In specific, ResiComp develops a bi-directional masked Transformer to model the contextual dependencies among latents with dual-functionality: 1) iteratively acts as a conditional entropy model to boost compression efficiency;2) operates latent PLC to improve resilience. During training, we integrate MVTM to mirror the effects of packet loss, enabling a dual-functional Transformer to restore the masked latents by predicting their missing values and conditional probability mass functions. Our ResiComp jointly optimizes compression efficiency and loss resilience. Moreover, ResiComp provides flexible coding modes, allowing for explicitly adjusting the efficiency-resilience trade-off in response to varying Internet or wireless network conditions. Extensive experiments demonstrate that ResiComp can significantly enhance the NIC’s resilience against packet losses, while exhibits a worthy tradeoff between compression efficiency and packet loss resilience. Additionally, packet-level simulations, conducted using diverse network models based on real traces, demonstrate that ResiComp exhibits much better robustness to fluctu

关键词： Packet loss

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SoundSpring: Loss-Resilient Audio Transceiver with Dual-Functional Masked Language Modeling

arXiv

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arXiv 2025年

作者： Yao, Shengshi Dai, Jincheng Qin, Xiaoqi Wang, Sixian Wang, Siye Niu, Kai Zhang, Ping The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, we propose "SoundSpring", a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Joint Parabolic Interpolation and Barycenter Calibration of Spatial Spectra – A High Precision Sensing Solution with Near-Field MIMO Systems

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IEEE Transactions on Communications 2025年

作者： Zhao, Baoyue Wang, Xin Yang, Yiran Liu, Xiqing Liu, Yuanwei Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Next-generation mobile communication systems will employ higher frequency bands and larger antenna arrays to meet the growing demand for data rates. However, this shift will extend the Rayleigh distance, resulting in near-field effects. Traditional far-field algorithms for source sensing yield considerable errors under these conditions. Near-field spatial spectrum estimation algorithms require multi-dimensional spectral peak searches, which lead to high computational complexity and necessitate a trade-off between sensing accuracy and real-time performance. Consequently, there is an urgent need for high-precision, low-complexity algorithms suitable for near-field sensing. This study proposes a calibration algorithm for spectral peak searches in near-field spatial spectra, referred to as the joint parabolic interpolation and barycenter calibration (PI-BC) algorithm. Simulation results indicate that, compared to existing parameter estimation algorithms, the joint PI-BC algorithm significantly improves sensing accuracy. Furthermore, the computational complexity of the calibration process in the joint PI-BC algorithm is negligibly low. © 1972-2012 IEEE.

关键词： Interpolation

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NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission

arXiv

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arXiv 2025年

作者： Yue, Weijie Si, Zhongwei Wu, Bolin Wang, Sixian Qin, Xiaoqi Niu, Kai Dai, Jincheng Zhang, Ping Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling flexible variable-rate joint source-channel coding and efficient bandwidth allocation aligned with the NeRF semantic feature’s different contribution to the 3D scene synthesis fidelity. Experimental results demonstrate that NeRFCom achieves free-view 3D scene efficient transmission while maintaining robustness under adverse channel conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel coding

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Synergistic Task-Offloading in 6g Edge Networks Based on Propagation Dynamics

SSRN

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SSRN 2025年

作者： Zhu, Chao Zhang, Yuexia Wang, Xinyi Zhu, Xuzhen The Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

In future 6G edge networks, Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) can fully utilize the idle resources of smart terminals and alleviate the burden on backhaul links. However, the limited idle resources of smart terminals and the over-reliance on D2D-assisted computation offloading may result in a large number of terminals experiencing task overload, which could lead to the risk of edge network paralysis. To address these issues, this paper establishes a Service-Auxiliary-Request-Healing (SARH) task-offloading propagation model based on propagation dynamics theory. This model describes the dynamic transmission process of offloaded tasks in 6G edge networks and constructs two linear threshold functions to characterize the differences in task processing capabilities between user terminals and edge servers. Furthermore, the proposed task-offloading propagation model is theoretically analyzed using edge compartment theory, and the propagation dynamics equations are established to derive the saddle point and critical conditions leading to task overload in a large number of user terminals, providing theoretical guidance for preventing network paralysis. Finally, simulation results show that the SARH model effectively describes the task-offloading propagation process in edge networks, and by controlling key factors such as the proportion of user terminals selecting D2D-assisted MEC synergistic task-offloading, network connectivity density, and network degree distribution heterogeneity, network paralysis can be avoided. © 2025, The Authors. All rights reserved.

关键词： Computation offloading

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A Fairness-aware Incentive Framework for Heterogeneous Federated Learning with Bifurcated Reverse Auction Design

A Fairness-aware Incentive Framework for Heterogeneous Feder...

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IEEE Conference on Wireless Communications and networking

作者： Shaolong Chen Sizhe Huang Zan Zhou Xiping Li Shujie Yang Yi Sun Changqiao Xu Beijing University of Posts and Telecommunications Shenzhen Institute Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Federated Learning (FL) is an emerging distributed learning framework designed to address isolated data island and protect privacy. Besides, Clustered Federated Learning (CFL) is introduced as an efficient multitask scheme to solve heterogeneous problems in FL where clients' data is distributed in non-i.i.d. (non-independent and identically distributed) scenarios. However, due to bandwidth limitation and latency tolerance, the server can only select a subset of clients to participate. Average selection and only selecting low heterogeneous client groups lead to severe results. How to fairly select clients and improve efficient model performance in heterogeneous scenarios with limited communication has become a key issue. We propose a fairness-aware clustered federated learning (FACFL) incentive framework which balances collective and individual fairness. Specifically, our framework models CFL as a bifurcated reverse auction that consists of a first-layer cluster auction and a second-layer client auction. Our framework can dynamically adjust the par-ticipation of clusters and clients according to the communication capabilities. The experimental results on the CIFAR-10 dataset demonstrate that FACFL improves the model performance in severely heterogeneous and communication limited scenarios. Additionally, FACFL can maintain a high level of the training fairness with different numbers of clients.

关键词： Training Computer aided instruction Data privacy Federated learning Distance learning Distributed databases Bandwidth Servers

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Recent development on online public opinion communication and early warning technologies: Survey

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Expert Systems with Applications 2025年 284卷

作者： Wu, Wei Yang, Yawen Qiao, Tianlu Peng, Haipeng School of Information Science and Engineering Shandong Normal University Jinan China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

The rapid development of the Internet and communication technologies has led to the emergence of online public opinion, which plays a crucial role in disseminating information and guiding the public. However, due to its large volume of data and fast-paced changes, automated monitoring of online public opinion is challenging. Currently, advancements in artificial intelligence (AI) technology provides new solutions for detecting and early warning of online public opinion. This paper reviews the literature in the field of online public opinion research over the years. Firstly, it systematically elucidates the formation mechanism and dissemination model of online public opinion. Secondly, it focuses on the analysis of public opinion monitoring technology in the era of artificial intelligence, clarifying the research branches of online public opinion early warning technology. Finally, it identifies the shortcomings in current research on online public opinion and proposes directions for future research. © 2025 Elsevier Ltd

关键词： Reviews

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Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey

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IEEE Internet of Things Journal 2025年第10期12卷 13384-13397页

作者： Zhang, Tao Kong, Fanyu Deng, Dongshang Tang, Xiangyun Wu, Xuangou Xu, Changqiao Zhu, Liehuang Liu, Jiqiang Ai, Bo Han, Zhu Deng, Robert H. Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui243002 China Minzu University of China School of Information Engineering Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Jiaotong University School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Singapore Management University Department of Information Systems Singapore Singapore

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network. © 2014 IEEE.

关键词： Cloud security

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Distributed Fine- Tuning of Foundation Models Over Heterogeneous Edge Devices

Distributed Fine- Tuning of Foundation Models Over Heterogen...

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IEEE Conference on Wireless Communications and networking

作者： Sunder Ali Khowaja Yixuan Li Yuxuan Hu Xiaoqi Qin Kaifeng Han State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Electronic Information Engineering Taiyuan University of Technology Taiyuan China China Academy of Information and Communications Technology

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The synergy between Federated Learning (FL) and Foundation Models (FMs) holds great promise in enhancing privacy protection and improving the generalization capabilities of AI systems. However, the high computational and communication overhead of FMs hinders effective deployment in real-world scenarios. Although some pioneering research has proposed using proxy sub-Foundation Models (sub-FMs) to reduce the computational and communication costs when fine-tuning FMs in FL environments, it overlooks the challenges posed by heterogeneous mobile devices with varying computational and communication capabilities, and by dynamic changes in their operational conditions, which cause very long FL training delay. Motivated by these challenges, we propose a novel federated fine-tuning of Foundation Models design via adaptive pruning (FedFTAP). FedFTAP introduces a pruning method specifically designed for FMs, combined with parameter-efficient fine-tuning modules to enhance communication and computational efficiency. FedFTAP further addresses system heterogeneity and system dynamic changes by adaptively tailoring heterogeneous sub-FMs suitable for local training on mobile devices. Moreover, FedFTAP introduces a method for aligning heterogeneous sub-FMs with the global FM. The experimental results show that FedFTAP effectively reduces computational and communication costs in federated fine-tuning scenarios.

关键词： Training Privacy Frequency modulation Costs Foundation models Federated learning System dynamics Mobile handsets Protection Tuning

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CrossSim: Towards Cross-System Trajectory Similarity Computation via Representation Learning

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IEEE Internet of Things Journal 2025年

作者： Cao, Zijian Zhao, Dong Dong, Xiyuan Wang, Qiyue Yuan, Haitao Gao, Yujia Ma, Huadong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Zhongguancun Laboratory Beijing100094 China

Trajectory similarity computation is essential for various downstream applications, such as anomaly route detection, order matching, and digital contact tracing. However, its effectiveness is confined within a single system due to privacy concerns associated with sharing raw trajectories across different systems. In this paper, we propose CrossSim, a novel framework designed to efficiently retrieve similar trajectories across all systems while preserving individual privacy. Our framework comprises three main components: i) a Trajectory Encoding Model that transforms trajectories into high-quality representations, where similarity relationships are reflected by their distances;ii) a two-stage optimization mechanism, including a Contrastive Similarity Learning stage and a Federated Similarity Learning stage, that alleviates the impact of heterogeneous similarity relationships across different systems on model training without aggregating raw trajectories;iii) a Similar Trajectory Retrieval procedure that obtains top-k similar trajectories from all systems without sharing raw trajectories. We conduct comprehensive experiments on three real-world datasets to evaluate the effectiveness of our proposed framework. The evaluation results demonstrate that CrossSim outperforms all existing schemees. © 2014 IEEE.

关键词： Federated learning

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