Vehicular Communications最新文献

{"title":"3D position error bound for wideband localization systems with application to UAVs","authors":"Alberto Facheris,&nbsp;Luca Reggiani","doi":"10.1016/j.vehcom.2025.100909","DOIUrl":"10.1016/j.vehcom.2025.100909","url":null,"abstract":"<div><div>Positioning is a crucial element of the new connected world and its integration with the current and future releases of the mobile system technology has been gaining interest since the introduction of the 5G verticals. In the application layer, positioning provides an additional and, in many cases, a fundamental value for the implementation of services like terrestrial and non-terrestrial vehicles, industrial robots and many other advanced systems. In this work we present a study on the formulation of the 3D Position Error Bound (PEB) based on Time-of-Arrival measures. We extend the analysis of the localization Cramer Rao Bound to the 3D geometry including the impacts of multipath, multiple anchors, clock and position offsets. In addition, we elaborate the result, providing a deeper insight on potential applications of this analytical tool, as positioning of Unmanned Aerial Vehicles in areas covered by 5G signals.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100909"},"PeriodicalIF":5.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cooperative caching scheme utilizing regional feature and dynamic vehicle clustering in vehicular edge networks","authors":"Yujian Chen,&nbsp;Weidi Tian,&nbsp;Zhengle Li,&nbsp;Hui Song","doi":"10.1016/j.vehcom.2025.100912","DOIUrl":"10.1016/j.vehcom.2025.100912","url":null,"abstract":"<div><div>Empowered with edge caching technology, the near-end storage resources of edge nodes in the Internet of Vehicles (IoV) can be fully utilized to accelerate the process of responding to content requests. In this paper, we propose a cooperative edge caching scheme utilizing regional feature and dynamic vehicle clustering (CRFDC). We take into consideration the fact that the change frequency of content popularity and regional feature is much lower than that of network topology and channel changes affected by vehicle mobility. To address this, we establish a double time-scale model. On the larger time-scale, we consider changes in content popularity and regional feature. On the smaller time-scale, we use the Prediction by Partial Matching (PPM) algorithm to predict vehicle's position. Additionally, we implement a dynamic cluster management approach, where vehicles with similar paths are grouped together, and use a consistent hashing algorithm to distribute contents among cooperative nodes. Finally, we employ deep reinforcement learning (DRL) approach to optimize our cooperative caching strategy for achieving lower content delivery latency. Simulation experiments demonstrate that our CRFDC scheme outperforms other cooperative caching schemes and benchmark algorithms in terms of reducing content transmission delay, improving cache hit ratio and decreasing communication overhead.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100912"},"PeriodicalIF":5.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A vehicular network multipath management algorithm based on vehicular traffic","authors":"Changxin Liu,&nbsp;Yu Qiu,&nbsp;Min Chen,&nbsp;Yantao Cai","doi":"10.1016/j.vehcom.2025.100906","DOIUrl":"10.1016/j.vehcom.2025.100906","url":null,"abstract":"<div><div>Internet of Vehicles (IoV) uses a variety of communication technologies to connect vehicles, road infrastructure, and the cloud to achieve real-time information exchange and intelligent interaction between vehicles, vehicles and infrastructure, vehicles and service providers so as to improve traffic safety, traffic efficiency, and ride experience. Some scholars have proposed the use of Multipath Transmission Control Protocol (MPTCP) in IoV to improve its performance. However, researchers ignore the factors that influence it, such as user preferences, business characteristics, and actual needs, when constructing the transmission algorithm of IoV. In order to solve the above problems, this paper proposes a vehicular Network path management algorithm based on vehicular traffic, which includes two algorithms: the vehicular Network path management algorithm based on path characteristics and signal characteristics (PCSC) and the vehicular Network path management algorithm based on path characteristics and traffic service (PCTS). To verify the performance of the proposed algorithm, we designed a simulation experiment of vehicle movement in Mininet-WiFi software. The results show that the PCSC algorithm has higher path selection flexibility than the traditional MPTCP algorithm and the existing path transmission algorithms. The performance of the PCTS algorithm is more precise than the PCSC algorithm, which improves the efficiency and throughput of the network and solves the communication needs of users in different periods.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100906"},"PeriodicalIF":5.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint subcarrier assignment and power allocation for UAV-assisted air-ground integrated full-duplex OFDMA networks","authors":"Tong Wang","doi":"10.1016/j.vehcom.2025.100907","DOIUrl":"10.1016/j.vehcom.2025.100907","url":null,"abstract":"<div><div>The self-interference caused by simultaneous uplink and downlink transmissions, along with inter-cell co-channel interference, significantly challenges the benefits of full-duplex transmission in future multi-UAV assisted Air-Ground Integrated OFDMA Networks. Effective resource allocation is crucial for achieving high system performance in these complex full-duplex environments. This paper investigates the joint optimization of subcarrier scheduling and power assignment, a task complicated by nonconvex Quality of Service (QoS) constraints, the nonconvex nature of the objective function, and the combinatorial intricacies of subcarrier scheduling. To overcome these difficulties, we first propose a Time-Sharing Greedy Rounding algorithm (TS-GR) based on the alternating optimization (AO) method. To further enhance the solution quality, we also propose an <span><math><msub><mrow><mi>l</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>-norm regularization-based algorithm (LP-NR). Extensive simulation results and theoretical analyses confirm the convergence and efficiency of our proposed methods in UAV-assisted full-duplex OFDMA networks. The simulations highlight that while TS-GR can achieve higher rates under relaxed QoS requirements, LP-NR offers robust performance by consistently satisfying both uplink and downlink QoS requirements. Our findings demonstrate that the gains of multi-cell full-duplex wireless networks over their half-duplex counterparts are significant under optimal conditions but can be constrained by high self-interference and noise levels.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100907"},"PeriodicalIF":5.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent and efficient Metaverse rendering and caching in UAV-aided vehicular edge computing","authors":"Linlin Yuan ,&nbsp;Guoquan Wu ,&nbsp;Kebing Jin ,&nbsp;Ya Li ,&nbsp;Jianhang Tang ,&nbsp;Shaobo Li","doi":"10.1016/j.vehcom.2025.100904","DOIUrl":"10.1016/j.vehcom.2025.100904","url":null,"abstract":"<div><div>The extensive application of the Metaverse in the Internet of Vehicles (IoV) has provided broader application scenarios and innovative opportunities for intelligent vehicle travel. The implementation of the Metaverse, which necessitates low latency, high precision, and swift feedback and interaction, can be effectively addressed by harnessing unmanned aerial vehicle (UAV)-assisted IoV technology. However, the actual wireless communication environment of UAV-assisted IoV networks, characterized by variability and complexity amidst numerous uncertain and uncontrollable interference factors, underscores the urgent need for research on the efficient communication and computing within the Metaverse. In this work, we investigate an efficient rendering scheme for Metaverse applications in UAV-aided edge computing networks, where multiple UAVs perform various Metaverse applications for vehicles with the help of a ground base station. Considering image quality and frame refresh rate as key metrics, we formulate a joint system utility optimization problem to minimize response time and energy consumption. To provide stable and high-quality vehicular Metaverse services, we develop an intelligent rendering and caching method for intelligent vehicular Metaverse, where a diffusion probabilistic model-based Metaverse frame rendering algorithm and a deep learning-based Metaverse frame caching algorithm are jointly designed. The proposed method can achieve optimal resource allocation results with low time complexity by fully exploring the benefits of a double auction model between vehicles and UAVs and a social model between different vehicles. Based on real-world datasets, we conduct extensive simulation experiments. Numerical results indicate that the proposed algorithm can improve resource utilization and reduce Metaverse frame rendering time and system energy consumption significantly.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100904"},"PeriodicalIF":5.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"5G NR sidelink time domain based resource allocation in C-V2X","authors":"Mehnaz Tabassum,&nbsp;Aurenice Oliveira","doi":"10.1016/j.vehcom.2025.100902","DOIUrl":"10.1016/j.vehcom.2025.100902","url":null,"abstract":"<div><div>This study explores the need for efficient resource allocation in fifth generation (5G) New Radio (NR) sidelink communication for cellular vehicle-to-everything (C-V2X) applications. With the advent of 5G networks, C-V2X can enable direct connection between neighboring vehicles and infrastructure without relying on the cellular network. However, direct communication between devices in 5G NR sidelink makes resource allocation more challenging than in a cellular network. Efficient resource allocation is essential to maintain dependable communication, especially in crowded and interference-prone contexts. There are different type of resource allocation methods such as time-domain, frequency-domain, and power-domain resource allocation, which can be used separately or in combination to achieve efficient resource allocation. In this study, the authors discuss time domain based resource allocation method based on packet generation time and packet allocation time. The implications of efficient resource allocation in 5G NR sidelink in C-V2X include increased signal-to-noise ratio, reduced interference, lower latency, and increased network capacity. The proposed approach is demonstrated on a Network Simulator (NS3.34) along with the traffic scenarios generated using Simulated Urban Mobility (SUMO). Our results demonstrate that time allocation is a promising approach to achieve efficient resource allocation, enabling safer and more effective transportation systems for C-V2X applications.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100902"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A question-centric review on DRL-based optimization for UAV-assisted MEC sensor and IoT applications, challenges, and future directions","authors":"Oluwatosin Ahmed Amodu ,&nbsp;Raja Azlina Raja Mahmood ,&nbsp;Huda Althumali ,&nbsp;Chedia Jarray ,&nbsp;Mohd Hirzi Adnan ,&nbsp;Umar Ali Bukar ,&nbsp;Nor Fadzilah Abdullah ,&nbsp;Rosdiadee Nordin ,&nbsp;Zuriati Ahmad Zukarnain","doi":"10.1016/j.vehcom.2025.100899","DOIUrl":"10.1016/j.vehcom.2025.100899","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) applications vary widely including data monitoring, data collection and analysis, intelligent navigation and object tracking, surveillance and emergency response, vehicular and intelligent transport, and agricultural, marine, and photogrammetry. Mobile Edge Computing (MEC)-based UAV-assisted IoT networks enable resource-constrained mobile or IoT devices to offload computationally demanding tasks to UAVs or edge nodes with more computing power in order to improve battery consumption, performance, or Quality of Service. UAV-assisted IoT applications generally require a lot of precision for efficient UAV control involving UAV movement and position optimization and Deep Reinforcement Learning (DRL) has recently been identified as one of the most prominent techniques for facilitating this and optimizing of the terrestrial network performance, thus improving the operation of these applications. This paper aims to answer twelve important research questions relating to the research on DRL for Mobile Edge Computing (MEC)-based UAV-assisted sensor and IoT applications from 47 systematically selected articles. The questions cover a variety of topics including the UAV-assisted MEC IoT applications studied, variants of deployed DRL, the purpose of DRL, Markov Decision Processes (MDPs) components, unique network architectural features, environments and integrated technologies, role of UAVs, optimization constraints, joint optimization frameworks, energy-management techniques, metrics examined, benchmark algorithms and performance results as well as identified probable future considerations based on the review. Lastly, the challenges and future directions of DRL application in UAV-assisted MEC systems are discussed. This paper aims to provide both communication generalists and optimization specialists with a comprehensive understanding of the research landscape in this field.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100899"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Task offloading and multi-cache placement based on DRL in UAV-assisted MEC networks","authors":"Kai Xue,&nbsp;Linbo Zhai,&nbsp;Yumei Li,&nbsp;Zekun Lu,&nbsp;Wenjie Zhou","doi":"10.1016/j.vehcom.2025.100900","DOIUrl":"10.1016/j.vehcom.2025.100900","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are being developed as a promising technology to assist mobile edge computing (MEC) systems due to their reliable wireless communication, flexible computing service capabilities, and flexible deployment. However, in the face of huge information and demanding task delay, it is a challenging problem to reduce the system cost. This paper studies task offloading and cache space placement for ground users, and proposes a multi-UAV assisted computing framework, which is a four-layer transmission system composed of ground users (UE), UAVs, edge data centers (EDC) and remote clouds. By jointly optimizing UAV cache space, flight path, offloading decision, channel ratio, and battery power, we formulate the problem to minimize the long-term average weighted cost of the system under the constraint of cache space and computing resources. Since this problem is a mixed integer variable problem, we design a task offloading and cache placement algorithm based on deep reinforcement learning, namely the Cooperative Long-term Average Cost Minimization Optimization Algorithm (CLACMO). Firstly, we transform the mixed action variable space by using embedded tables and conditional variational autoencoders (VAE) combined with latent space, and map the mixed action variable to the latent action space. This approach effectively unifies discrete and continuous actions, addressing the challenge of mixed action spaces that traditional deep reinforcement learning algorithms struggle with. Secondly, based on the deep reinforcement learning (DRL), we achieve the long-term system average weighted cost minimization more efficiently under the constraints of task offloading and cache placement. The results show that compared with the PER-UOS-RL, MASAC, and MADDPG algorithms, the average reward has increased by 54.5%, 66.7%, and 69.7% respectively, and the average task completion rate has increased by 12.9%, 38.1%, and 9.11% respectively, demonstrating the effectiveness of our novel method.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100900"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing task offloading in vehicular networks: A multi-agent cloud-edge-device framework","authors":"Peiying Zhang ,&nbsp;Enqi Wang ,&nbsp;Lizhuang Tan ,&nbsp;Neeraj Kumar ,&nbsp;Jian Wang ,&nbsp;Kai Liu","doi":"10.1016/j.vehcom.2025.100898","DOIUrl":"10.1016/j.vehcom.2025.100898","url":null,"abstract":"<div><div>In vehicular networks, the increasing demand for computational resources often exceeds the capabilities of in-vehicle devices. To address these challenges, we propose a cloud-edge-device collaborative framework integrated with a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm for dynamic optimization of task offloading and resource allocation. Experimental evaluations demonstrate the proposed algorithm's superiority over traditional methods, achieving an 11% reduction in energy consumption and a 23% increase in task completion rate compared to local processing-only strategies, while reducing average task delay by 50% relative to static offloading approaches. The MADRL-based framework not only ensures efficient task distribution but also adapts to fluctuating network conditions, achieving a resource utilization rate of 85%. These findings underscore its potential to enhance performance in intelligent transportation systems by balancing computational efficiency, energy consumption, and task latency.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100898"},"PeriodicalIF":5.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing task offloading in MIMO-enabled vehicular networks through deep reinforcement learning","authors":"Jian Xu,&nbsp;Shengchao Su","doi":"10.1016/j.vehcom.2025.100901","DOIUrl":"10.1016/j.vehcom.2025.100901","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) effectively alleviates the computational burden faced by vehicles in processing compute-intensive tasks due to resource limitations. However, traditional approaches typically employ coarse-grained task offloading strategies that utilize sequential protocols and discrete action spaces, resulting in high latency and increased energy consumption. These limitations render such strategies unsuitable for real-time applications. To address these challenges, an innovative computation offloading strategy is proposed, specifically designed to minimize the long-term average computation cost in a multi-vehicle, multi-server Internet of Vehicles (IoV) system. The MEC system model is constructed using Multiple-Input Multiple-Output (MIMO) technology, which facilitates simultaneous uplink transmissions from all vehicles, significantly reducing the time required for data uploads. Subsequently, a continuous action space is adopted to enhance both the flexibility and precision of decision-making. Additionally, Batch-Constrained Q-learning (BCQ) is introduced to further constrain the actions taken by the policy, mitigating overly optimistic estimates through a batch constraint mechanism. Finally, the Twin Delayed Deep Deterministic Policy Gradient with Batch-Constrained Q-learning (TD3BCQ) framework is developed to enable fine-grained decision-making for local execution and power allocation during task offloading within a continuous action space. Experimental results demonstrate that the proposed scheme achieves a more balanced offloading strategy and better exploits the available computing resources, leading to an approximate 20% improvement compared to the baselines.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100901"},"PeriodicalIF":5.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}