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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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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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Efficient Semantic-aware Encryption for Secure Communications in Intelligent Connected Vehicles

arXiv

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arXiv 2025年

作者： Wang, Bizhu Bian, Zhiqiang Chen, Yue Xu, Xiaodong Sun, Chen Zhang, Wenqi Zhang, Ping State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Broadband Communication Peng Cheng Laboratory Shenzhen China Research Laboratory China

Semantic communication (SemCom) significantly improves inter-vehicle interactions in intelligent connected vehicles (ICVs) within limited wireless spectrum. However, the open nature of wireless communications introduces eavesdropping risks. To mitigate this, we propose the Efficient Semantic-aware Encryption (ESAE) mechanism, integrating cryptography into SemCom to secure semantic transmission without complex key management. ESAE leverages semantic reciprocity between source and reconstructed information from past communications to independently generate session keys at both ends, reducing key transmission costs and associated security risks. Additionally, ESAE introduces a semantic-aware key pre-processing method (SA-KP) using the YOLO-v10 model to extract consistent semantics from bit-level diverse yet semantically identical content, ensuring key consistency. Experimental results validate ESAE’s effectiveness and feasibility under various wireless conditions, with key performance factors discussed. Copyright © 2025, The Authors. All rights reserved.

关键词： Secure communication

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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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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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Coordinating Communication and Computing for Wireless VR in Open Radio Access Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Hongtao Chen, Ziqi Guo, Fengxian Li, Nan Sun, Yaohua Peng, Mugen Liu, Yuanwei Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China China Mobile Research Institute Beijing100053 China Hong Kong

Driven by diverse applications, radio access networks (RAN) are expected to embrace built-in computing and intelligence, forming a versatile wireless computing platform that closely integrates communication and computing. To fully unleash the potential of such a synergistic system, it is essential to coordinate communication and computing with intelligence unlocked by the radio intelligent controllers (RICs) in O-RAN. Building on the groundwork established by existing theoretical studies and simulations, we develop a platform that can emulate the events in the real-world system in more detail, bringing theoretical works closer to practical implementation. In this paper, we first introduce ns-GP-O-RAN, a software simulation platform developed over ns-3, enabling communication, computation task processing, large-scale data collection, and testing of system-level orchestration policies through user-level control. Taking virtual reality (VR) as an example, we formulate the computation offloading problem and develop a prediction-based computation offloading xAPP, which contains a prediction phase to predict users' end-to-end (E2E) performance with the deep neural network and a system-level decision-making phase for global orchestration with the differential evolution algorithm. We evaluate the system capacity and E2E latency over the developed ns-GP-O-RAN, which is more effective than existing approaches. © 2025 IEEE.

关键词： Radio access networks

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Multi-Agent Moth-Flame Reinforcement Learning based Broadcast Beam Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Huang, Shan Yao, Haipeng Mai, Tianle Wu, Di Xu, Jiaqi Yu, F. Richard Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Carleton University Depart. Systems Computer Engineering Ottawa Canada

Currently, beamforming antenna array technologies are of utmost importance in 5G communication systems. These technologies are essential for optimizing the coverage and signal quality of the cellular network. However, the optimization of broadcast beams presents significant challenges due to the complex strategy profile space. Each beam can be configured with different widths and heights, making it difficult for conventional algorithms to handle. To address this issue, we propose a novel approach called Multi-Agent Moth-Flame Reinforcement Learning (MAMF-RL) algorithm for broadcast beam optimization. MAMF-RL combines reinforcement learning and moth-flame optimization algorithms to interactively search for the optimal broadcast beams. By decomposing the problem into multiple single-sector antenna configuration problems, MAMF-RL effectively reduces the algorithm complexity. We conducted experiments utilizing real data in an 18-sector wireless coverage area. To evaluate the performance of our proposed method, we compared it with traditional methods such as the particle swarm algorithm. The results demonstrate that our MAMF-RL model achieves an average coverage rate of 1.82% higher and a 13.74% lower overlapping coverage rate compared to traditional methods. © 2025 IEEE. All rights reserved.

关键词： 5G mobile communication systems

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Learning-Based Task-Centric Multi-User Semantic Communication Solution for Vehicle Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Yuan, Yifan Zhang, Jingxuan Xu, Xiaodong Wang, Bizhu Han, Shujun Sun, Mengying Zhang, Ping University of Science and Technology Beijing National School of Elite Engineering Beijing100083 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

With the application of 5 G in in-vehicle network scenarios, the inevitable crisis of scarcity of communication resources is deepening. At the same time, considering the high demands for communication delay in vehicular network scenarios, it is essential to meet the requirements for reliable information transmission in high-speed mobility environments. To address these issues, we propose the Task-Centric Multi-User Semantic Communication (TCMSC) solution, designed to meet the energy consumption and transmission time delay requirements in line with the new paradigm of semantic communication. Our solution introduces a task-centric semantic processing model aimed at improving Semantic Spectral Efficiency (S-SE). The TCMSC solution is tailored for multi-service vehicle scenarios, optimizing power consumption and enhancing reliability, making it well-suited for challenging environments. Moreover, we propose a novel method using Stochastic Network Calculus (SNC) to accurately model semantic task delay and calculate the upper bound of Vehicle-to-Infrastructure (V2I) delay-bound violation probability. However, to tackle the increased optimization complexity from SNC while simultaneously enhancing feature extraction, we propose the Transformer Advantage Actor Critic (TR-A2C) algorithm. This algorithm leverages the Transformer to capture dynamic vehicle parameters across scenarios, accelerating the TCMSC solution. Experimental results demonstrate that, compared to traditional single-service vehicle dispatch, TCMSC improves delay violation probability by 2.88 and reduces power consumption by 31.1all while effectively enhancing S-SE in complex traffic environments. © 1967-2012 IEEE.

关键词： Stochastic systems

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Statistical Channel Estimation for Holographic MIMO Exploiting the Clustered Sparsity

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IEEE Transactions on Vehicular technology 2025年

作者： Guo, Yuqing Guo, Xufeng Chen, Yuanbin Wang, Ying Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

This paper investigates the statistical channel state information (S-CSI) estimation for holographic multiple-input-multiple-output (HMIMO) systems. Instead of acquiring S-CSI based on instantaneous channel state information (I-CSI), we directly estimate S-CSI by leveraging its clustered sparsity. Specifically, the inherent clustered sparsity of HMIMO S-CSI is characterized by von Mises-Fisher (vMF) distribution, given arbitrary number, position and angular spread of scatterers. The number of parameters to be estimated can be compressed to the order of scatterers' quantities from the order of degrees of freedom (DoFs) of the array. Then, a novel wavenumber-domain expectation-maximization (WD-EM) algorithm is proposed to implement cluster-by-cluster variational inference, which significantly reduces the computational complexity. Finally, simulation results validate the robustness of the proposed scheme with respect to pilot overhead and signal-to-noise ratio (SNR). © 1967-2012 IEEE.

关键词： Channel estimation

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Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems

arXiv

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arXiv 2025年

作者： Wang, Yihao Li, Lingxiao Tang, Yifan Zhang, Ru Liu, Jianyi the School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China the State Key Laboratory of Networking and Switching Technology Beijing100876 China

Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger public safety and cause social losses. Therefore, this paper proposes a watermarking that can verify the integrity of copyright in response to the needs of ITS, termed ITSmark. ITSmark focuses on functions such as extracting watermarks, verifying permission, and tracing tampered locations. The scheme uses the copyright information to build the multi-bit space and divides this space into multiple segments. These segments will be assigned to tokens. Thus, the next token is determined by its segment which contains the copyright. In this way, the obtained data contains the custom watermark. To ensure the authorization, key parameters are encrypted during copyright embedding to obtain cipher data. Only by possessing the correct cipher data and private key, can the user entirely extract the watermark. Experiments show that ITSmark surpasses baseline performances in data quality, extraction accuracy, and unforgeability. It also shows unique capabilities of permission verification and tampered location tracing, which ensures the security of extraction and the reliability of copyright verification. Furthermore, ITSmark can also customize the watermark embedding position and proportion according to user needs, making embedding more flexible. © 2025, CC BY-NC-SA.

关键词： Advanced traffic management systems

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