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A novel routing method for dynamic control in distributed computing power networks

Digital Communications and Networks 2024年第6期10卷 1644-1652页

作者： Lujie Guo Fengxian Guo Mugen Peng The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power *** with both packet transmission and data processing,it requires joint optimization of communications and *** the diverse requirements of applications,we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power *** from traditional routing protocols,additional metrics related to computing are taken into consideration in the proposed *** on the multi-attribute decision theory and the fuzzy logic theory,we propose two routing selection algorithms,the Fuzzy Logic-Based Routing(FLBR)algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making(l PMADM)*** results show that the proposed policy could achieve better performance in average processing delay,user satisfaction,and load balancing compared with existing works.

关键词： Computing power networks Routing Fuzzy logic Multi-attribute decision making

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Coverage and reliability analysis of multi-IRS-aided distributed space-time block coding in MISO wireless communication systems

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PHYSICAL COMMUNICATION 2024年 63卷

作者： Wang, Xudong Feng, Lei Zhou, Fanqin Li, Wenjing BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Intelligent reflecting surface (IRS) can realize information manipulation and signal modulations to perform space-time coding wireless communication. This article introduces a novel model to perform space-time block coding (STBC), where the base station transmits multiple symbols to a destination through multiple IRS that are utilized to adjust the phase shift and establish strong links. To maximize the end-to-end SNR, we propose a graph theory based method to obtain the optimal transmit antenna selection (TAS) scheme. Based the optimal TAS scheme, we derive the statistical characterizations of the sum of independent generalized gamma random variables to obtain end-to-end signal-to-noise ratio expressions. Then, the closed-form expressions of outage probability (OP) and symbol error rate (SER) are formulated respectively to evaluate the coverage and reliability performance in wireless communication systems. Furthermore, the asymptotic expressions for OP and SER at a high transmission power are given, then the diversity order is discussed to observe the performance limit. To verify this theoretical performance analysis, extensive computer simulations are conducted to prove its effectiveness. The simulation results show that the multi-IRS-aided STBC system can attain the same or even better performance as the STBC system based on multiple amplify-and-forward relays using a little number of reflecting elements. More interestingly, the OP and SER of this system decrease more than that of the relays-aided system as transmission power increases.

关键词： Intelligent reflecting surface Space-time block coding Outage probability Symbol error rate Phase shift Relay

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The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

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Computers, Materials & Continua 2024年第6期79卷 4883-4904页

作者： Jingting Mei Yang Yang Zhipeng Gao Lanlan Rui Yijing Lin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network *** the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is ***,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN *** neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data *** by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of ***,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy ***,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN *** on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis ***,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model *** conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.

关键词： Network fault diagnosis edge networks Izhikevich neurons pruning dynamic spike timing dependent plasticity learning

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DDPG-AdaptConfig: A deep reinforcement learning framework for adaptive device selection and training configuration in heterogeneity federated learning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Yu, Xinlei Gao, Zhipeng Xiong, Zijian Zhao, Chen Yang, Yang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Federated Learning (FL) is a distributed machine learning approach that protects user privacy by collaboratively training shared models across devices without sharing their raw personal data. Despite its advantages, FL faces issues of increased convergence time and decreased accuracy due to the heterogeneity of data and systems across devices. Existing methods for solving these issues using reinforcement learning often ignore the adaptive configuration of local training hyperparameters to suit varying data characteristics and system resources. Moreover, they frequently overlook the heterogeneous information contained within local model parameters. To address these problems, we propose the DDPG-AdaptConfig framework based on Deep Deterministic Policy Gradient (DDPG) for adaptive device selection and local training hyperparameters configuration in FL to speed up convergence and ensure high model accuracy. Additionally, we develop a new actor network that integrates the transformer mechanism to extract heterogeneous information from model parameters, which assists in device selection and hyperparameters configuration. Furthermore, we introduce a clustering-based aggregation strategy to accommodate heterogeneity and prevent performance declines. Experimental results show that our DDPG-AdaptConfig achieves significant improvements over existing baselines.

关键词： Federated learning Data heterogeneity System heterogeneity Reinforcement learning Deep deterministic policy gradient

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PFDP: privacy-preserving federated distillation method for pretrained language models

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-16页

作者： Chen, Chaomeng Su, Sen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Pre-trained language models (PLMs) have emerged as transformative tools in natural language processing. However, their training typically requires massive datasets that are distributed across multiple nodes, posing significant challenges for centralized training paradigms. Federated pre-trained language models (FL-PLMs) offer a promising solution by enabling collaborative model training without raw data. However, they suffer from significant performance degradation due to statistical heterogeneity across clients. Although knowledge distillation has been proposed to address this challenge by aggregating knowledge instead of model parameters, existing approaches remain vulnerable to privacy leakage and potential knowledge degradation during the distillation process. To overcome these limitations, we propose Privacy-preserving Federated Distillation for Pre-trained language models (PFDP), a novel framework designed to optimize both model performance and privacy preservation. PFDP introduces a Local Distillation with Differential Privacy (LoTDP) that safeguards client data privacy. Furthermore, we propose a Global Aggregation via Transfer Learning (GATeL) algorithm that improves the global model's generalization capabilities while mitigating the catastrophic forgetting phenomenon commonly associated with federated distillation. Extensive evaluation across multiple classification tasks demonstrates the effectiveness of PFDP in improving accuracy while preserving privacy.

关键词： Federated learning Pretrained language models Privacy-preserving

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

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IEEE TRANSACTIONS ON VEHICULAR technology 2025年第1期74卷 1433-1445页

作者： Yuan, Shuo Sun, Yaohua Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R 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%.

关键词： Array signal processing Satellite broadcasting Base stations Backhaul networks Satellites Optimization Multicast algorithms Power demand Space-air-ground integrated networks Low earth orbit satellites Beam direction control cache placement multicast beamforming satellite-terrestrial networks (STNs)

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TextDiff: Enhancing scene text image super-resolution with mask-guided residual diffusion models

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PATTERN RECOGNITION 2025年 164卷

作者： Liu, Baolin Yang, Zongyuan Chiu, Chinwai Xiong, Yongping Beijing Univ Post & Telecommun State Key Lab Switching & Networking Technol Beijing 100876 Peoples R China

The goal of scene text image super-resolution (STISR) is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for STISR. It contains two modules: the Text Enhancement Module (TEM) and the Mask- Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets, with a maximum improvement of 2.0% in recognition accuracy over existing methods while enhancing the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training.

关键词： Scene text image super-resolution Text enhancement Diffusion model Multi-stage learning Model expandability

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Hybrid Channel Tracking for THz Massive MIMO Communication Systems in Dynamic Environments

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第3期43卷 988-1003页

作者： Fan, Yuheng Yang, Chuang Sun, Yanran Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With gigahertz-level bandwidth, terahertz (THz) holds promise for achieving exceptionally high transmission rates in prospective sixth-generation (6G) communications. However, considerable path loss poses an obstacle to THz communications. To compensate for this, massive multiple-input-multiple-output (MIMO) based beamforming is utilized to promote directional power with narrow beams in communications. In dynamic environments, the frequent adjustment of narrow beams results in fast time-varying channel state information (CSI), which constrains the application of the THz communication systems. While traditional deterministic-based and statistical-based channel tracking methods address different aspects of this issue, they suffer from balancing accuracy and complexity in the THz dynamic environments. To solve this problem, based on the cluster distribution of THz time-varying channel, we propose a novel hybrid channel tracking method that uses deterministic physical motion variation law to extract the cluster subspace, and then statistical Markov evolution models are applied within it. To achieve this, an integrated clustering and estimation method, clustering subspace matching pursuit (CSMP) is proposed for obtaining the channel clusters prior knowledge. Then based on above hybrid tracking method design, we propose a virtual cluster subspace turbo-approximate message passing (VCS-TAMP). Finally, several simulation results validate that our proposal achieves great improvement in both accuracy and computational time performance.

关键词： Terahertz communications Vectors Massive MIMO Accuracy Heuristic algorithms Estimation Tracking Solid modeling Mathematical models Computational modeling Terahertz massive MIMO channel tracking

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Propagation dynamics of vacillating individual with behavioral variation on double-layer social network

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PHYSICS LETTERS A 2025年 530卷

作者： Xu, Wenlong Lu, Kejian Zhu, Xuzhen Beijing Univ Post & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

This paper investigates behavior propagation in a two-layer network, emphasizing the roles of behavioral vacillation and dependency psychology. By integrating a threshold function model and percolation theory, the study explores how varying degrees of vacillation and network viscosity impact behavior spread. The results show that higher behavioral vacillation accelerates propagation, reduces uncertainty, and lowers the critical threshold, highlighting the significant influence of psychological factors on diffusion. These findings provide valuable insights for real-world applications in marketing strategies, social influence, and public opinion dynamics. Future work should incorporate dynamic network elements to offer a more comprehensive understanding of behavior propagation.

关键词： Vacillation Double-layer network Network viscosity Dependency psychology

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Self-Sustainable Reconfigurable Intelligent Surface-Empowered D2D Communication Network

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2025年第2期10卷 287-299页

作者： Yang, Zhixiang Feng, Lei Zhou, Fanqin Xie, Kunyi Qiu, Xuesong Li, Wenjing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The reconfigurable intelligent surface (RIS) is a green and promising technology that provides passive beamforming through a large amount of low-power reflecting elements, to realizes expected coverage extension and interference signal suppression. In this paper, we investigate a self-sustainable RIS-empowered D2D communication network, where the RIS first harvests energy from the D2D signals, and then uses energy collected to sustain its passive beamforming operation. We aim to characterize the energy efficiency (EE) maximization under imperfect channel state information conditions by jointly optimizing the transmit precoding in both two stages, RIS passive beamforming design, and energy harvesting time allocation. An efficient alternating optimization algorithm is proposed to deal with the difficult non-convex optimization problem. Specifically, transmit precoding is optimized by using the Dinkelbach's method, Lagrangian dual transform, quadratic transform and S-procedure. The penalty convex-concave procedure is adopted to solve the optimal phase shift of RIS. A closed-form expression for the optimal energy harvesting duration is derived. The simulation results show that the proposed scheme further enhances the EE compared with the active RIS and no RIS schemes in various scenarios.

关键词： Device-to-device communication Reconfigurable intelligent surfaces Energy harvesting Array signal processing Resource management Interference Green computing D2D communication energy efficiency maximization reconfigurable intelligent surface self-sustainable

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