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IEEE Conference on Wireless Communications and networking

作者： Zhipeng Gao Leyu Han Qian Luo Ze Chai Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Data heterogeneity and privacy leakage pose inevitable challenges for federated learning in real-world scenarios. These obstacles have motivated the development of numerous personalized federated learning algorithms aimed at acquiring personalized models for individual clients. However, previous work in achieving personalization has typically proposed solutions from the perspective of the entire model or from the level of layer within the network structure. Diverging from them, we introduce a novel perspective, scrutinizing the personalization issue with finer granularity at the parameter level. In this paper, we propose PGPFL, a novel personalized federated learning framework incorporating differential privacy, which rapidly achieves the required local accuracy while providing privacy protection. PGPFL introduces configuring a guard for each parameter of the local network to constrain local updates for personalized learning, and calculate the importance value of each parameter of the current model during the local training process to determine the private parts. Additionally, PGPFL propose a valid client selection mechanism based on the response ratio and local accuracy to decrease single-round training time, enhancing training efficiency. Extensive experiments conducted under various simulated heterogeneous environments demonstrate that, in comparison with existing algorithms, our algorithm improves both average local accuracy and training efficiency.

关键词： Training Differential privacy Privacy Accuracy Laplace equations Federated learning Noise Protection Edge computing Convergence

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Self-Adaptive Dynamic In-Band network Telemetry Orchestration for Balancing Accuracy and Stability

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IEEE Transactions on network and Service Management 2025年第2期22卷 1514-1530页

作者： Ouyang, Tianhao Yao, Haipeng He, Wenji Mai, Tianle Wang, Fu Yu, F. Richard Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing Institute of Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing University of Posts and Telecommunications School of Electronic and Engineering Beijing100876 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In-band network telemetry (INT) is an emerging network measurement technique that offers real-time and fine-grained visualization capabilities for networks. However, the utilization of INT for network measurement introduces additional overheads to the network. The process of data collection consumes extra bandwidth resources, and adjustments to the data collection scheme can impact network stability. Additionally, the INT orchestration scheme requires adaptation to dynamics in the network to improve measurement accuracy. Therefore, striking a balance between accuracy and stability becomes a critical problem. In this paper, our focus lies in the trade-off between measurement accuracy and network stability. We consider the long-term orchestration of multiple telemetry tasks, rationally deploying distinct telemetry tasks to different application flows. To address the challenge, we propose a self-adaptive Dynamic INT Orchestration scheme, D-INTO. Specifically, we formulate a stochastic optimization problem for dynamic INT orchestration. Then we employ Lyapunov optimization to decouple the stochastic optimization problem and use surrogate Lagrangian relaxation to construct a polynomial-time approximation algorithm. Theoretical analysis and experimental results demonstrate that our proposed D-INTO outperforms existing schemes in terms of adaptability to the network dynamics. © 2004-2012 IEEE.

关键词： Polynomial approximation

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Efficient Spatio-Temporal Predictive Learning for Massive MIMO CSI Prediction

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ZTE Communications 2025年第1期23卷 3-10页

作者： CHENG Jiaming CHEN Wei LI Lun AI Bo School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China ZTE Corporation Shenzhen 518057China

Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband *** massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user *** address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction *** employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal *** addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel *** results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.

关键词： massive MIMO deep learning CSI prediction CSI feedback

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Physical Modeling of Reconfigurable Intelligent Surface for Channel Modeling

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China Communications 2025年第2期22卷 128-142页

作者： MiaoWei Dou Jianwu Cui Yijun Yang Zhenyu School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Mobile Network and Multimedia Technology Shenzhen 518005China ZTE Corporation Shenzhen 518005China

In this paper,a physical model of RIS of bistatic polarized radar cross section is derived starting from the Stratton-Chu equations under the assumptions of physical optics,PEC,far field and rectangular RIS *** the context of important physical characteristics of the backscattering polarization of RIS,the modeling of the RIS wireless channel requires a tradeoff between complexity and accuracy,as well as usability and *** channel modeling of RIS systems,RIS is modelled as multi-equivalent virtual base stations(BSs)induced by multi polarized electromagnetic waves from different incident *** comparison between test and simulation results demonstrates that the proposed algorithm effectively captures the key characteristics of the general RIS element polarization physical model and provides accurate results.

关键词： channel modeling map-based hybrid channel model polarized model Reconfigurable intelligent surface(RIS)

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Torrent: Re-Architecting End-to-End Transmission for Cross-Datacenter RDMA networks

Torrent: Re-Architecting End-to-End Transmission for Cross-D...

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IEEE Conference on Wireless Communications and networking

作者： Haoyu Pan Yuan Li Zirui Wan Jiao Zhang Tao Huang State key Laboratory of Networking and Switching Technology BUPT China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Sustainability is becoming increasingly challenging in today's data centers with limited space, power and connectivity. Large cloud service providers interconnect geographically distributed datacenters for better scalability and availability. Applications running on cross-datacenter network impose great challenges in transport design. In this paper, we identify two inherent limitations of extending the existing transport technology, RDMA, and its Ethernet derivative, RoCE, to long-haul transmission. First, the on-chip resources of commodity RDMA NICs are insufficient for long-haul transmission. Second, applying existing traffic control schemes to inter-datacenter environment exhibits poor performance. Motivated by this, we propose Torrent, a switch-driven transport framework which partitions end-to-end control into three sub-control loops. To achieve the combined goals of fairness and high performance in cross-datacenter scenarios, Torrent employs fast acknowledgment and near-end congestion control on datacenter interconnection (DCI) switches. We implement Torrent prototypes on commodity programmable switches and evaluate it through real-world testbed experiments. Our results show that Torrent can achieve high link utilization over ultra-long distances and quickly converge congested flows to steady rates.

关键词： Data centers Scalability Prototypes Switches Ethernet Traffic control System-on-chip Sustainable development Testing

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ACL: Adaptive Edge-Cloud Collaborative Learning for Heterogeneous Devices with Unlabeled Local Data

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Zhengyuan Zhao, Dong Liu, Renhao Yao, Yuxing Li, Xiangyu Ma, Huadong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science Beijing100876 China

Edge-cloud collaborative learning emerges as a promising paradigm for adapting pre-trained deep neural network (DNN) models to the ever-changing edge data environments and specific downstream tasks. However, the heterogeneity of edge devices and unlabeled local data hinder the effectiveness of existing collaborative learning approaches. To address the above issues, we propose ACL, a novel adaptive edge-cloud collaborative learning paradigm for heterogeneous devices with unlabeled local data. In ACL, we first use FedNAS, a neural architecture search algorithm designed for collaborative learning to generate a customized model on each participating device, and then a lightweight semi-supervised collaborative learning framework HSSCL is used to fine-tune the pre-trained DNN model. Compared with the SOTA collaborative learning approaches, ACL achieves significant accuracy improvement, averaging 31.5% for image classification and 15.5% for object detection. Furthermore, it reduces time overhead by 3.1-5.1× and memory overhead by 6.3-12.5×. We will release our models and tools. © 2002-2012 IEEE.

关键词： Contrastive Learning

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Synchronous Multi-Modal Semantic Communication System With Packet-Level Coding

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3684-3697页

作者： Tian, Yun Ying, Jingkai Qin, Zhijin Jin, Ye Tao, Xiaoming Peking University School of Electronics Beijing100091 China Tsinghua University Department of Electronic Engineering Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, the synchronization and packet-level forward error correction (FEC) of multimodal semantics have not been well studied. Synchronizing multimodal features in both the semantic and time domains is challenging due to the independent design of semantic encoders. In this paper, we take the facial video and speech transmission as an example and propose a Synchronous Multi-modal Semantic Communication System with Packet-Level Coding (SyncSC). To achieve semantic and time synchronization, 3D Morphable Mode (3DMM) coefficients and text are transmitted as semantics. We propose a semantic codec that achieves similar reconstruction quality with lower bandwidth. The visual-guided speech synthesis is designed to synchronize video, text and speech. We propose a packet-Level FEC method for video semantics, called PacSC, that maintains visual quality even at high packet loss rates. For text packets, a text packet loss concealment module, called TextPC, based on Bidirectional Encoder Representations from Transformers (BERT) is proposed, which improves the performance of traditional FEC methods. Simulation results show that SyncSC reduces transmission overhead while ensuring high-quality synchronous transmission of video and speech over the packet loss network. © 2002-2012 IEEE.

关键词： Semantic communication Synchronization Encoding Packet loss Receivers Speech recognition Streaming media Forward error correction Transmitters Speech synthesis

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Cache-Aware Cooperative Multicast Beamforming in Dynamic Satellite-Terrestrial networks

arXiv

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arXiv 2025年

作者： Yuan, Shuo Sun, Yaohua Peng, Mugen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Cache-aided STNs are acknowledged as a promising paradigm for accelerating content delivery to users and alleviating the load of backhaul links. However, the dynamic nature of low earth orbit (LEO) satellites and the complex interference among satellite beams and terrestrial base stations pose challenges in effectively managing limited edge resources. To address these issues, this paper proposes a method for dynamically scheduling caching and communication resources, aiming to reduce network costs in terms of transmission power consumption and backhaul traffic, while meeting user QoS demands and resource constraints. We formulate a mixed timescale problem to jointly optimize cache placement, LEO satellite beam direction, and cooperative multicast beamforming among satellite beams and base stations. To tackle this intricate problem, we propose a two-stage solution framework, where the primary problem is decoupled into a short-term content delivery subproblem and a long-term cache placement subproblem. The former subproblem is solved by designing an alternating optimization approach with whale optimization and successive convex approximation methods according to the cache placement state, while cache content in STNs is updated using an iterative algorithm that utilizes historical information. Simulation results demonstrate the effectiveness of our proposed algorithms, showcasing their convergence and significantly reducing transmission power consumption and backhaul traffic by up to 52%. Copyright © 2025, The Authors. All rights reserved.

关键词： Base stations

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Satellite-Terrestrial Integrated Fog networks: Architecture, Technologies, and Challenges

arXiv

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arXiv 2025年

作者： Yuan, Shuo Peng, Mugen Sun, Yaohua State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

In the evolution of sixth-generation (6G) mobile communication networks, satellite-terrestrial integrated networks emerge as a promising paradigm, characterized by their wide coverage and reliable transmission capabilities. By integrating with cloud-based terrestrial mobile communication networks, the limitations of low Earth orbit (LEO) satellites, such as insufficient onboard computing capabilities and limited inter-satellite link capacity, can be addressed. In addition, to efficiently respond to the diverse integrated tasks of communication, remote sensing, and navigation, LEO constellations need to be capable of autonomous networking. To this end, this article presents a satellite-terrestrial integrated fog network for 6G. Its system architecture and key technologies are introduced to achieve flexible collaboration between fog satellites and terrestrial cloud computing centers. In particular, key techniques with diverse challenges and their corresponding solutions are discussed, including integrated waveform design and resource management based on fog satellite onboard processing, as well as mobility management and native artificial intelligence based on cloud-fog collaboration. Finally, future challenges and open issues are outlined. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite links

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Intelligent Telemetry: P4-Driven network Telemetry and Service Flow Intelligent Aviation Platform 20th

Intelligent Telemetry: P4-Driven Network Telemetry and Serv...

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20th IFIP WG 10.3 International Conference on network and Parallel Computing, NPC 2024

作者： Gao, Jing Zhou, Fanqin Dong, Mianxiong Feng, Lei Ota, Kaoru Li, Zijian Fan, Jiawei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran050-8585 Japan Beijing University of Posts and Telecommunications Address Beijing Beijing100876 China

ISBN: (纸本)9789819628292

To address the current challenges in network monitoring and management, such as poor real-time performance, high cost, low accuracy, and slow fault detection and recovery, a P4-driven network telemetry and resource scheduling platform is designed and presented in this paper. This platform embeds telemetry information within data packets, achieving real-time monitoring and anomaly detection of network states. It significantly enhances the granularity and timeliness of network monitoring and employs a path rerouting mechanism for rapid network recovery. The platform boasts high scalability and customizability, allowing on-demand adjustment of telemetry strategies and parameters. It provides low-overhead, business-level telemetry information, offering comprehensive support for diverse services and advancing network management and service quality. This work provides a technical foundation for constructing the next generation of intelligent, efficient, and reliable networks through continuous research and practical application. © IFIP International Federation for Information Processing 2025.

关键词： Telemetering

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