Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
VTT Technical Research Centre of Finland Ltd（芬兰VTT技术研究中心）；University of Electro-Communications（电气通信大学）；Zhejiang University（浙江大学）；Shizuoka University（静冈大学）；University of Oslo（奥斯陆大学）；University of Oulu（奥卢大学）
Xianfu Chen, Celimuge Wu, Tao Chen, Honggang Zhang, Zhi Liu, Yan Zhang, Mehdi Bennis
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.
An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
Institute for Safe Autonomy and School of Physics, Engineering and Technology（安全自主研究所和物理、工程与技术学院）；School of Electrical and Electronic Engineering（电气与电子工程学院）；University of Leeds（利兹大学）；School of Computer Science（计算机科学学院）；University College Dublin（都柏林大学学院）
SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services
Middlesex University London（伦敦密德萨斯大学）；IIIT-Hyderabad（海得拉巴国际信息技术学院）；IIIT Hyderabad（海得拉巴国际信息技术学院）；Manchester Metropolitan University（曼彻斯特城市大学）；Dublin City University（都柏林城市大学）
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Department of Electrical and Computer Engineering, Auburn University（奥本大学电气与计算机工程系）
Huawei Technologies Duesseldorf GmbH（华为技术杜塞尔多夫有限公司）；Technische Universität Berlin（柏林工业大学）
Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), …
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Information Science and Technology University（北京信息科技大学）；University of Technology Sydney（悉尼科技大学）
Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. In this paper, we expand the control domain to a large area with multiple intersections. In particular, we propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-…
Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks
The Ohio State University（俄亥俄州立大学）；BTS Group（BTS集团）；Istanbul Technical University（伊斯坦布尔理工大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Edinburgh Napier University（爱丁堡龙比亚大学）
VTT Technical Research Centre of Finland（芬兰VTT技术研究中心）；University of Electro-Communications（电气通信大学）；Zhejiang University（浙江大学）；University of Oslo（奥斯陆大学）；University of Oulu（奥卢大学）
This paper targets at the problem of radio resource management for expected long-term delay-power tradeoff in vehicular communications. At each decision epoch, the road side unit observes the global network state, allocates channels and schedules data packets for all vehicle user equipment-pairs (VUE-pairs). The decision-making procedure is modelled as a discrete-time Markov decision process (MDP). The technical challenges in solving an optimal control policy originate from highly spatial mobility of vehicles and temporal variations in data traffic. To simplify the decision-making process, we …
Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, …
Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Department of Electrical and Computer Engineering, Auburn University（奥本大学电气与计算机工程系）
Connectivity Management in Satellite-Aided Vehicular Networks with Multi-Head Attention-Based State Estimation
Interdisciplinary Research Center for Intelligent Secure Systems, KFUPM, Saudi Arabia（智能安全系统跨学科研究中心，KFUPM，沙特阿拉伯）；Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106319, Taiwan（计算机科学与信息工程系，国立台湾大学，台北106319，台湾）；Department of Communication Engineering, National Central University, Taoyuan 320317, Taiwan（通信工程系，国立中央大学，桃园320317，台湾）
AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover
State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, China（先进轨道交通自主运行国家重点实验室，北京交通大学，中国）；School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China（电子与信息工程学院，北京交通大学，北京，中国）；Mediatek (Beijing) Inc, Beijing, China（联发科技（北京）有限公司，北京，中国）；Centre for Wireless Innovation (CWI), Queen’s University Belfast, UK（无线创新中心（CWI），女王大学贝尔法斯特校区，英国）
Huazhong Univ. of Science & Technology（华中科技大学）；University of South Florida（南佛罗里达大学）
Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connect…
The University of Edinburgh（爱丁堡大学）；Virginia Tech（弗吉尼亚理工大学）
LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing WiFi net…
Trustworthy Semantic Communication for Vehicular Networks: Challenges and Solutions
School of Cyber Science and Engineering, Xi’an Jiaotong University（网络安全与工程学院，西安交通大学）；College of Science and Engineering, Kanazawa University（科学与工程学院，金泽大学）；Faculty of Science and Technology, University of Macau（科学与技术学院，澳门大学）
Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing
Aix-Marseille University（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；LIS（信息科学与系统实验室）；Université Libre de Bruxelles（布鲁塞尔自由大学）；WEL Research Institute（WEL 研究院）；University of Leeds（利兹大学）；School of Computer Science（计算机学院）
