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ROTR: Role-Transformable Multi-Agent Resource Allocation for Nonstationary Vehicular Communications

IEEE Transactions on Mobile Computing 2025年

作者： Li, Yang Yuan, Quan Fu, Xiaoyuan Luo, Guiyang Kang, Jiawen Li, Jinglin Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Efficient wireless resource allocation is essential for supporting multi-vehicle cooperation. The service data exchanged among intelligent vehicles is typically diverse, with varying transmission requirements that shift according to applications and traffic conditions, leading to major fluctuation in communication situations. Existing multi-agent reinforcement learning based resource allocation methods are often inefficient in handling such nonstationary communication situations due to their rigid cooperation patterns. To this end, we propose a ROle-TRansformable multi-agent resource allocation method, named ROTR. This method adopts a hierarchical decision-making process, where a high-level agent at a base station (BS) dynamically plans and distributes cooperation roles (CRs) and cooperation behaviors (CBs) in response to fluctuating communication situations. The Low-level agents within the transmitting vehicles (TVs) perform role transformations based on the assigned CRs and subsequently receive behavioral guidance according to CBs, enabling dynamic adjustments in cooperation patterns to adapt to variable communication situations and make resource allocation decisions. Additionally, we introduce a non-BS-assisted mode based on policy distillation, which enables a seamless transition to independent operation without the BS, relying solely on local states to generate CRs and CBs, thereby facilitating global resource cooperation. Extensive simulation experiments demonstrate that the proposed framework optimizes resource efficiency in nonstationary vehicular communications. © 2002-2012 IEEE.

关键词： Resource allocation

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Cryptanalysis and improvement of a certificateless encryption scheme in the standard model

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Frontiers of Computer Science 2014年第1期8卷 163-173页

作者： Lin CHENG Qiaoyan WEN Zhengping JIN Hua ZHANG State Key Laboratory of Networking and Switch Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Certificateless public key cryptography elimi- nates inherent key escrow problem in identity-based cryptog- raphy, and does not yet requires certificates as in the tradi- tional public key infrastructure. In this paper, we give crypt- analysis to Hwang et al.＇s certificateless encryption scheme which is the first concrete certificateless encryption scheme that can be proved to be secure against ＂malicious-but- passive＂ key generation center （KGC） attack in the stan- dard model. Their scheme is proved to be insecure even in a weaker security model called ＂honest-but-curious＂ KGC at- tack model. We then propose an improved scheme which is really secure against ＂malicious-but-passive＂ KGC attack in the standard model.

关键词： certificateless public key cryptography stan-dard model provable security

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AoI-Delay Tradeoff in Mobile Edge Caching: A Lyapunov Optimization-Based Method

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IEEE Internet of Things Journal 2025年第11期12卷 16266-16281页

作者： Li, Ran Huang, Chuan Qin, Xiaoqi Fu, Zhanhong Yang, Lei Yang, Dong The Future Network of Intelligence Institute The Chinese University of HongKong Shenzhen518172 China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Future Network of Intelligence Institute The Chinese University of HongKong Shenzhen518172 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China SF Technology Shenzhen518052 China The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This paper investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an O(1/V) versus O(V) tradeoff for the average AoI and average service delay. © 2014 IEEE.

关键词： Stochastic systems

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LMSR: A Low-Jitter Multiple Slots Routing Algorithm in LEO Satellite Networks

LMSR: A Low-Jitter Multiple Slots Routing Algorithm in LEO S...

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IEEE Conference on Wireless Communications and networking

作者： Shiran Sun Ran Zhang Kai Liu Zekun Sun Qinqin Tang Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China State Key Laboratory of Space Network and Communications Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In recent years, Low Earth Orbit (LEO) constellation based networks have attracted wide attention from both the academia and the industry. Broadband access and backhaul becomes a typical application for LEO satellite networks. However, the high-speed motion of satellites brings periodic fluctuation of delays, which is a great violation to the Quality of Service (QoS) provisioning. In this work, inspired by the success of Software Defined networking (SDN), and considering the dynamics of LEO satellite networks, we propose a low-jitter multiple slots routing in LEO satellite networks to provide low-jitter end-to-end delay path computing and control. The proposed multiple slots routing optimization mechanism takes account of the topology shift as well as the dynamic propagation delay across multiple topology snapshots, and thus achieves low-jitter performance. The performance improvement of the proposed mechanism is validated by the simulation, which promises the value of jitter under 30 ms.

关键词： Satellites Network topology Software algorithms Low earth orbit satellites Quality of service Jitter Routing Delays Topology Software defined networking

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From local verification to global reasoning: Exploiting slot-accompanying update for improved slot selection

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Knowledge-Based Systems 2025年 319卷

作者： Qian, Bing Guo, Jinyu Wang, Qiwei Shuang, Kai China Telecom Corporation Limited Beijing Research Institute Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

The goal of dialogue-state tracking (DST) is to determine the current state of a dialogue by analysing the entire preceding dialogue context. Nonetheless, current approaches frequently fail to account for the significance of concurrent updates, where related slots must be updated simultaneously based on their historical relationships, even in the absence of explicit signals in the current dialogue turn. To address this limitation, we introduce From Local Verification to Global Reasoning (FLV2GR), an innovative method that improves slot-update selection by combining local verification of present dialogue details with global reasoning over historical dialogue data. Our approach utilizes a graph neural network (GNN) to model and infer interdependencies between slots, enabling the identification of accompanying update relationships that are frequently overlooked by other approaches. This comprehensive selection mechanism improves the precision of slot updates, thereby enhancing overall DST performance. The FLV2GR model establishes a new performance benchmark on the MultiWOZ 2.1, 2.2, and 2.4 datasets, showcasing its effectiveness in capturing both local and global dialogue dynamics for more precise and reliable DST.1 © 2025 Elsevier B.V.

关键词： Graph neural networks

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Time-Space-Varying Resource Graph-Based Dependent Task Offloading for Satellite-Terrestrial Integrated Computing Power Networks

Time-Space-Varying Resource Graph-Based Dependent Task Offlo...

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IEEE Conference on Wireless Communications and networking

作者： Xin Zou Renchao Xie Qinqin Tang Zehui Xiong Gaochang Xie Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the continuous advancement of network technologies and hardware devices, computation-intensive and latency-sensitive tasks have emerged worldwide, requiring networks to provide extensive coverage, low latency, and robust computing capabilities. Leveraging the global coverage of LowEarth Orbit (LEO) satellites and the flexible resource invocation capabilities of the Computing Power Network (CPN), we propose a Satellite-Terrestrial Computing Power Network (ST-CPN) framework that integrates both strengths. In this framework, tasks can be offloaded to satellites closer to users for processing, ensuring high-quality services anytime and anywhere. However, due to the dynamic nature of the network and the limited resources of individual nodes, efficiently executing complex dependent tasks presents significant challenges. Therefore, we investigate the dependent task offloading problem in the dynamic ST-CPN environment. Considering dynamic changes of topology and available resources caused by satellite mobility, we propose a Time-Space-Varying Resource Graph (TSVRG) to capture the status of the communication, storage, and computation resources. On this basis, given that individual nodes struggle to process dependent tasks, we offload multiple subtasks of a task to different nodes for collaborative processing. In this paper, we model the task as a Directed Acyclic Graph (DAG) and transform the offloading problem into a mapping problem from the DAG to TSVRG. We then introduce a Delay Predictionbased Graph Mapping Algorithm (DPGMA) to address this problem. Simulation results indicate that our scheme achieves better performance than the benchmark schemes.

关键词： Satellites Computational modeling Simulation Transforms Benchmark testing Space-air-ground integrated networks Dynamic scheduling Prediction algorithms Delays Topology

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Sensing-Centric Energy-Efficient Hybrid Beamforming Design for Terahertz Integrated Sensing and Communications

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IEEE Transactions on Vehicular technology 2025年

作者： Yang, Kai Xu, Jin Tao, Xiaofeng Sun, Mengying Wang, Bing Wu, Huici Beijing University of Posts and Telecommunications National Engineering Research Center of Mobile Network Technologies Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we study the sensing-centric energy-efficient hybrid beamforming (HBF) design for wideband terahertz (THz) integrated sensing and communications (ISAC) systems. To mitigate the spatial wideband effect and enhance energy efficiency (EE), a new sub-connected true-time delayers (TTDs)-based HBF architecture is introduced. We redefine a generalized metric, referred to as sensing-centric EE (SEE), for measuring "information per Joule"in radar-centric ISAC scenarios, such as smart healthcare and intelligent manufacturing. Our objective is to maximize SEE by optimizing the HBF, while satisfying the constraints of radar sensing mutual information (SMI), communication rate requirements, transmit power budget, and the hardware limitations of analog beamformers. To solve this non-convex problem, we develop an effective algorithm based on the penalty dual decomposition (PDD) framework combining the Dinkelbach, minorization-maximization (MM), successive convex approximation (SCA), and element-wise optimization methods. Simulation results illustrate that the proposed design can increase SEE by approximately 40% compared to the benchmark scheme. © 2025 IEEE.

关键词： Budget control

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WebARNav: Mobile Web AR Indoor Navigation with Edge-Assisted Vision Localization

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IEEE Transactions on Mobile Computing 2025年

作者： Huang, Yakun Meng, Shengwei Zhu, Yuanwei Wang, Jun Cao, Jacky Qiao, Xiuquan Su, Xiang Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Helsinki Department of Computer Science Helsinki Finland University of Helsinki Department of Agricultural Sciences Helsinki Finland

The gradual maturation of mobile augmented reality (AR) and localization technologies is enabling the development of immersive AR-enabled indoor localization and navigation systems. Existing indoor localization technologies (e.g., WiFi, infrared, Bluetooth) and navigation services do not provide intuitive 3D AR experiences and can be expensive to deploy. This paper introduces WebARNav, a cross-platform indoor localization system that provides user-friendly AR navigation services with low overhead and remarkable accuracy. First, we propose a lightweight location fusion framework for indoor navigation on the mobile web, which leverages accurate edge-supported vision localization to guide and correct lightweight pedestrian dead reckoning localization. Second, we improve the accuracy of localization using an attention-based feature extraction method and a dual-stream retrieval and co-visibility re-ranking technique for initial localization. Third, we significantly improve accuracy and speed up retrieval as users move by generating a topological map for traveling localization. We conducted extensive experiments on various indoor datasets to demonstrate localization accuracy and navigation experience. The study shows that WebARNav achieves a localization frequency of over 30 Hz and reduces the average trajectory error by 76% and 95% for single- and multi-floor office scenes, respectively, compared to the PDR-only method. The proposed traveling localization method also reduces the localization latency by 15.2%, 55.1%, and 98.6% in the baseline datasets, with an accuracy improvement of over 4%. © 2002-2012 IEEE.

关键词： Digital storage

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ReFrame: A Resource-Friendly Cloud-Assisted On-Device Deep Learning Framework for Vision Services

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IEEE Transactions on Services Computing 2025年

作者： Xie, Jianhang Ding, Chuntao Guan, Qingji Zhou, Ao Li, Yidong Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

Cloud-assisted Internet of Things (IoT) device deployment of deep neural networks (DNNs) promotes On-device deep learning to provide users with ubiquitous high-quality services by solving the contradiction between insufficient IoT device resources and intensive demand for high-performance DNN resources. However, most existing methods optimize DNNs by considering one or two terms of transmission, computation, and storage resources, but do not consider all three terms at the same time in cloud-assisted IoT device deployment and updating DNNs. To this end, we propose a non-learnable module-based ResNet and a cloud-assisted on-device deep learning framework, ReFrame, based on the consideration of three indicators: model transmission parameters, computation resources, and storage resources. In the proposed method, we first specify that some parameters in DNNs are non-learnable and randomly initialized, so that, these parameters can be saved and reproduced with a few random seeds. By doing so, the cloud only transmits random seeds and learnable parameters to reduce the number of parameter transmissions. Secondly, we reduce the computation resource consumption of the model by introducing computation-friendly operators, such as pooling, to replace vanilla convolutions. Finally, since random seeds are used to save non-learnable model parameters, on IoT devices we only need to store random seeds and learnable parameters to reproduce the well-trained model. Compared with saving the complete model, our method greatly reduces IoT device storage resource consumption. Experimental results on image classification, object detection, and semantic segmentation tasks demonstrate the effectiveness of the proposed method. Specifically, on the CIFAR-10, our proposed method reduces approximately 89% of FLOPs and 90% of transmitted data in the prototype system compared to ResNet-18. © 2008-2012 IEEE.

关键词： Cloud platforms

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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