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Graph Attentional Based Agglomerative Cluster for UAV Swarm networks

IEEE Transactions on network Science and Engineering 2025年

作者： Huang, Shan Yao, Haipeng Wang, Xiaoman Mai, Tianle Wang, Zunliang Guo, Song Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Hong Kong Polytechnic University Department of Computing Hong Kong

In recent years, UAVs have been widely employed in the construction of post-disaster emergency communication networks due to their flexible mobility and self-organizing capabilities, enabling them to quickly and spontaneously establish communication links. Post-disaster rescue efforts, restoring connectivity and communication, as well as providing disaster information, all require strong support from network applications. However, an essential element in the development of effective UAV swarm-based applications is the network system. Compared to the fixed networks, UAV swarm networks present unique challenges in design and implementation due to its characteristics of high mobility nodes, unstable links, and dynamic topology. Recently, clustering technology has gained recognition as an effective approach to constructing stable UAV swarm networks. In this paper, for UAV swarm networks, we propose a graph attention-based agglomerative clustering algorithm. This algorithm allows UAV nodes to learn similarity relationships through a graph attention network. By considering the mobility similarity between UAV nodes, each UAV node can merge with its adjacent nodes. Furthermore, we also design a cluster head selection algorithm based on mixed strategy games. The algorithm's effectiveness and accuracy were demonstrated by simulation results, which showed a 80.04% increase in network lifetime compared to baseline algorithms. © 2013 IEEE.

关键词： Disasters

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Robust Transmission Design for Covert Satellite Communication Systems With Dual-CSI Uncertainty

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IEEE Internet of Things Journal 2025年

作者： Jia, Huaiqi Wang, Ying Wu, Wen Yuan, Jun Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Frontier Research Center Shenzhen518000 China Joint Laboratory of Technology and Applications on Frequency Orbital Space Information Networks China Satellite Network System Research Institute Co Ltd Beijing100070 China

In this paper, we investigate a novel covert transmission scheme for satellite communication systems. Specifically, the satellite employs a rate-splitting multiple access technique to covertly transmit messages to multiple users and simultaneously transmit jamming signals, thereby enhancing the covert rate and robustness while avoiding detection by a warden. Due to the long propagation delay and lack of the warden's precise location information, acquiring perfect channel state information (CSI) between the satellite, covert users, and the warden is difficult. To this end, we establish a dual-CSI uncertainty model, which incorporates phase and norm-bounded uncertainty for the covert and wiretap channels to characterize the actual CSI. In addition, we formulate a stochastic optimization problem with the objective of maximizing the minimum covert rate while adhering to covert communication constraints. The optimization problem is non-convex and difficult to solve directly due to the dual-CSI uncertainty and the coupled nature of the variables. To solve the problem, we reformulate the original problem into a series of convex optimization problems by utilizing semidefinite relaxation, fractional programming, and the S-procedure methods. Then, we propose a robust Common Rate Allocation and Beamforming (CRAB) algorithm to obtain near-optimal solutions for the beamforming vectors and common rate allocation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms baseline schemes in terms of both covert rate and robustness. © 2014 IEEE.

关键词： Satellite communication systems

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Semantic Prior Aided Channel-Adaptive Equalizing and De-Noising Semantic Communication System with Latent Diffusion Model

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Bingxuan Han, Shujun Xu, Xiaodong Li, Weizhi Meng, Rui Dong, Chen Zhan, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies Beijing100876 China

Semantic Communication (SemCom) has opened a new paradigm in the 6G system. However, the performance of SemCom can be severely affected by time-varying path loss, channel noises, and other interference in wireless channels. Therefore, we propose a novel Semantic Prior aided Channel-adaptive Equalizing and De-noising SemCom (SP-EDNSC) framework, where adaptive elimination channel impact is regarded as an inverse problem. This inverse problem is addressed through semantic priors learned from score-based generative models cached in knowledge base. To reduce distortion while enhancing perceptual quality, we further combine autoencoders, adversarial learning and diffusion models to develop a latent diffusion-based (SP-Latent-Diff EDNSC) system within the SP-EDNSC framework. In the semantic space, the joint semantic equalizer and de-noiser module utilizes the proposed latent diffusion posterior sampling method. This method iteratively executes a modified reverse stochastic differential equation to sample clean semantic features, using the time-dependent score function of likelihood and semantic priors. The semantic priors are derived from pre-trained latent diffusion models, while the likelihood is approximated by a multivariate normal distribution. Simulations demonstrate that our scheme achieves superior performance in both distortion metrics like PSNR and SSIM, as well as in perceptual performance (LPIPS). © 2025 IEEE. All rights reserved.

关键词： Inverse problems

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HGTUL: A Hypergraph-based Model For Trajectory User Linking

arXiv

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arXiv 2025年

作者： Chang, Fengjie Zhu, Xinning Hu, Zheng Qin, Yang The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China

Trajectory User Linking (TUL), which links anonymous trajectories with users who generate them, plays a crucial role in modeling human mobility. Despite significant advancements in this field, existing studies primarily neglect the high-order inter-trajectory relationships, which represent complex associations among multiple trajectories, manifested through multi-location co-occurrence patterns emerging when trajectories intersect at various Points of Interest (POIs). Furthermore, they also overlook the variable influence of POIs on different trajectories, as well as the user class imbalance problem caused by disparities in user activity levels and check-in frequencies. To address these limitations, we propose a novel HyperGraph-based multi-perspective Trajectory User Linking model (HGTUL). Our model learns trajectory representations from both relational and spatio-temporal perspectives: (1) it captures high-order associations among trajectories by constructing a trajectory hypergraph and leverages a hypergraph attention network to learn the variable impact of POIs on trajectories;(2) it models the spatio-temporal characteristics of trajectories by incorporating their temporal and spatial information into a sequential encoder. Moreover, we design a data balancing method to effectively address the user class imbalance problem and experimentally validate its significance in TUL. Extensive experiments on three real-world datasets demonstrate that HGTUL outperforms state-of-the-art baselines, achieving improvements of 2.57%~20.09% and 5.68%~26.00% in ACC@1 and Macro-F1 metrics, *** Codes 68-07 Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Intrusion Detection for Intelligent Transportation Systems: A lightweight interpretable model

Intrusion Detection for Intelligent Transportation Systems: ...

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

作者： Yuxi Zhou Tao Feng Yazhuo Gao Yixuan Wu Lin Yang Jiaqi Lin Systems Engineering Institute AMS PLA Northwestern Polytechnical University State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Intrusion detection systems (IDSs) are essential in Intelligent transportation system (ITS) for detecting and identifying malicious activities. Although deep learning is commonly used in IDS, its limited interpretability hinders large-scale deployment. Additionally, the computational and storage constraints of ITS devices pose challenges to traditional IDS solutions. To address these issues, we propose RK-IDS, an interpretable IDS model combining rough set (RS) theory and the Kolmogorov-Arnold network (KAN). RS selects critical features without sacrificing detection accuracy, while KAN identifies patterns for precise classification. Performance metrics, including accuracy, precision, recall, false alarm, F1-score, and runtime, are derived from classification results. Ablation experiments using the CSE-CIC-IDS2018 and CIC-DDoS2019 datasets show that RK-IDS achieves accuracy of 95.20% and 96.88%, respectively. These results suggest RK-IDS offers better detection capabilities than traditional neural networks, making it highly applicable to modern ITS networks.

关键词： Training Accuracy Runtime Computational modeling Neural networks Intrusion detection Rough sets Transportation Feature extraction Data models

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A Transformer-Block-Wise Collaborative Training Mechanism with Hybrid Parallelism Over Heterogeneous networks

A Transformer-Block-Wise Collaborative Training Mechanism wi...

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IEEE Conference on Wireless Communications and networking

作者： Jiewei Chen Jingrong Wang Shaoyong Guo Jiakai Hao Xuesong Qiu Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications State Grid Beijing Information & Communication Company Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of AI-Generated Content (AIGC) services in wireless networks, efficient and high-quality distributed training of Large Language Models (LLMs) has become essential for enabling the large-scale application of next generation AI technologies. However, the extensive parameters of LLMs impose significant demands on memory, computing power and communication resources in heterogeneous networks. To efficiently utilize the dispersed network resources, this paper presents a First-Pipeline- Then-Federated Learning (FPTFL) approach with a hybrid parallel scheduling strategy to facilitate the training of Transformer-based LLMs. We propose a block-wise splitting mechanism to partition the Transformer's encoder into distinct segments, which are deployed cross individual devices. The encoder parameters and intermediate smashed data are uploaded to the edge server, where the whole model is updated through federated aggregation. Particularly, we develop a fine-grained computation-efficient method based on pipeline parallelism, enabling the segments to cooperatively train the entire encoder. An optimization problem is formulated to determine the LLM segments and the number of micro-batches under network resource constraints, with the goal of minimizing the total latency of LLM training services. Simulation results demonstrate that our approach enables Transformer-based model training on resource-constrained devices, preserves model performance, and reduces waiting time.

关键词： Training Performance evaluation Large language models Wireless networks Pipelines Parallel processing Transformers Heterogeneous networks Classification algorithms Optimization

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Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable network-Wide Traffic Signal Control

arXiv

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arXiv 2025年

作者： Zhang, Yifeng Liu, Yilin Gong, Ping Li, Peizhuo Fan, Mingfeng Sartoretti, Guillaume Department of Mechanical Engineering National University of Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Adaptive traffic signal control (ATSC) is crucial in reducing congestion, maximizing throughput, and improving mobility in rapidly growing urban areas. Recent advancements in parameter-sharing multi-agent reinforcement learning (MARL) have greatly enhanced the scalable and adaptive optimization of complex, dynamic flows in large-scale homogeneous networks. However, the inherent heterogeneity of real-world traffic networks, with their varied intersection topologies and interaction dynamics, poses substantial challenges to achieving scalable and effective ATSC across different traffic scenarios. To address these challenges, we present Unicorn, a universal and collaborative MARL framework designed for efficient and adaptable network-wide ATSC. Specifically, we first propose a unified approach to map the states and actions of intersections with varying topologies into a common structure based on traffic movements. Next, we design a Universal Traffic Representation (UTR) module with a decoder-only network for general feature extraction, enhancing the model’s adaptability to diverse traffic scenarios. Additionally, we incorporate an Intersection Specifics Representation (ISR) module, designed to identify key latent vectors that represent the unique intersection’s topology and traffic dynamics through variational inference techniques. To further refine these latent representations, we employ a contrastive learning approach in a self-supervised manner, which enables better differentiation of intersection-specific features. Moreover, we integrate the state-action dependencies of neighboring agents into policy optimization, which effectively captures dynamic agent interactions and facilitates efficient regional collaboration. Through comprehensive evaluations against other advanced ATSC methods across eight different traffic datasets, our empirical findings reveal that Unicorn consistently outperforms other methods across various evaluation metrics, highlighting its superiori

关键词： Topology

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Joint Precoding and Reflection Optimization for IRS-Assisted MU-MIMO-OTFS Downlink Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Zou, Tingting Wang, Fengyu Xu, Wenjun Zhang, Yimeng Pan, Miao Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Network and Switching Technology Beijing100876 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Mobile Research Institute Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Orthogonal time frequency space (OTFS) modulation has been proposed as a promising technique to achieve high-reliability communications in high-mobility scenarios. In this paper, we consider a multi-user (MU) multiple-input multiple-output (MIMO)-OTFS downlink communication scenario, where an intelligent reflecting surface (IRS) is deployed to enhance the performance. Specifically, the delay- Doppler (DD) domain input-output relationship of such a system is first derived, based on which, the joint optimization of the precoding matrices and IRS reflection phase is studied to maximize the weighted sum rate (WSR) considering user fairness. Note that the corresponding optimization problem is non-convex. To tackle this challenge, we first reformulate the problem into a more traceable equivalent form by the weighted mean minimum square error (WMMSE) approach. Utilizing the characteristics of the DD domain signal relationship, we then decouple the reformulated problem and further transform it into two subproblems, which are solved by the Lagrangian function method and the project-descent-retracting (PDR) algorithm, respectively. The precoding matrices and reflection phase are jointly optimized by alternative optimization to maximize WSR. Simulation results demonstrate that, compared with benchmark schemes, the proposed WMMSE-ALT can improve the rate more than 85% in IRS-assisted MU-MIMO-OTFS systems. © 1967-2012 IEEE.

关键词： Mean square error

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Deep Joint Source-Channel Coding Based on Feedback-Driven Codebook Optimization

Deep Joint Source-Channel Coding Based on Feedback-Driven Co...

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IEEE Conference on Wireless Communications and networking

作者： Weijie Zheng Haotai Liang Chen Dong Xiaodong Xu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Broadband Communication Peng Cheng Laboratory Shenzhen Guangdong China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The rapid growth of wireless communication technologies has made semantic communication an increasingly important approach for efficient data transmission. In this paper, a novel deep joint source-channel coding (DeepJSCC) framework based on Feedback-Driven Codebook Optimization (FDCO) for wireless image transmission is proposed. The framework dynamically optimizes the codebook based on channel feedback to improve image reconstruction performance under varying channel conditions. Specifically, a FDCO network is introduced to adjust the balance between common and individual information in the codebook based on the input signal-to-noise ratio (SNR). In addition, the residual between the original and quantized images is encoded to obtain semantic details, which are transmitted to reduce semantic quantization loss. Experimental results demonstrate that the proposed framework improves image quality and compression efficiency, especially under low SNR, and validates FDCO's dynamic adjustment of the codebook's information balance, leading to enhanced performance.

关键词： Wireless communication Image quality Image coding Quantization (signal) Image communication Semantic communication Robustness Optimization Image reconstruction Signal to noise ratio

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In-network DDoS Mitigation Mechanism for Vehicle Road Cooperation network With Victim-Centric Approach

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IEEE Internet of Things Journal 2025年

作者： Liu, Yan Shao, Sujie Guo, Shaoyong Zang, Zhibin Chen, Xingyu Qi, Feng Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China State Grid Information and Communication Industry Group Co. Ltd Beijing102211 China

Vehicle Road Cooperation (VRC) services closely related to personal safety impose stringent requirements on reliability and real-time performance. However, the growing trend of Vehicle to network (V2N) connections has significantly intensified the threat of distributed denial of service (DDoS) to the end-cloud communication links. To address this challenge, this paper introduces a comprehensive DDoS defense architecture for VRC, named In-network DDoS-oriented Balancer, Inspector, and Filter (IDBIF). Leveraging programmable switches, IDBIF adopts an in-network mode to dynamically mitigate volumetric DDoS traffic in real-time, which coordinates three data plane functions for traffic processing: load balancing, packet inspection, and traffic filtering. Our work faces the starved data plane resources and establishes the problem model for high reliability requirements of VRC services when subjected to DDoS attacks. Furthermore, we developed a Multi-Agent Hierarchical Structure Policy Gradient (MAHSPG) algorithm that combines the inherent characteristics of DDoS attacks to tackle the runtime deployment of high-dimensional and heterogeneous defense function chains. Simulation experiment results show that the proposed approach alleviates the data plane resource bottleneck and improves the DDoS defense effect by 24.80%, highlighting its advantages in ensuring reliable VRC services. © 2014 IEEE.

关键词： network security

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