Vehicular Communications最新文献

{"title":"A survey on physical layer security for 5G/6G communications over different fading channels: Approaches, challenges, and future directions","authors":"Parmila Devi,&nbsp;Manoranjan Rai Bharti,&nbsp;Dikshant Gautam","doi":"10.1016/j.vehcom.2025.100891","DOIUrl":"10.1016/j.vehcom.2025.100891","url":null,"abstract":"<div><div>The surge in wireless network attacks has intensified the focus on physical layer security (PLS) within academia and industry. As PLS provides security solutions by leveraging the randomness of wireless channels without the need for encryption/decryption keys, fading channels play a major role in PLS solutions. This survey aims to understand the effect of fading on PLS for 5G/6G communications by utilizing various PLS techniques such as beamforming, artificial noise injection, cooperative and opportunistic relaying, physical authentication, and intelligent reflective surface-based PLS over various fading channels. Initially, the role of PLS in 5G/6G communications, its fundamentals, and various techniques available for 5G/6G communications are examined. Since PLS for 5G communications has been extensively studied in the literature, we categorize it into two cases, direct and indirect communications, and provide a comprehensive survey on PLS for 5G communications over various fading channels. Thereafter, we survey the PLS for 6G communications over various fading channels, noting that the work available for PLS in 6G communications is limited and in its early stages. Given the increasing attention on artificial intelligence and machine learning (AI/ML) for wireless communications, this survey also explores PLS based on AI/ML techniques over various fading channels. Finally, the survey concludes with observations on challenges and future directions.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100891"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136343","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":"An efficient resource orchestration algorithm for enhancing throughput in fog computing-enabled vehicular networks","authors":"Md Asif Thanedar ,&nbsp;Sanjaya Kumar Panda","doi":"10.1016/j.vehcom.2025.100911","DOIUrl":"10.1016/j.vehcom.2025.100911","url":null,"abstract":"<div><div>The delay-sensitive applications, such as self-driving, smart transportation, navigation, and augmented reality assistance, can be evolved in vehicular ad-hoc networks (VANETs) using one of the leading paradigms, fog computing (FC). The intelligent vehicles are connected to the roadside infrastructure, such as high power nodes (HPNs) and roadside units (RSUs), also called fog nodes (FNs), for obtaining on-demand services. These FNs possess finite resources and can provide services to limited vehicles. However, when vehicles reach the network spike in demand, the FNs become impuissant in furnishing services in the existing solutions. As a result, there is a significant reduction in the network throughput. Therefore, we propose an efficient resource orchestration (ERO) algorithm to maximize the throughput by reducing the allocated resource blocks (RBs) of FNs. The ERO algorithm partitions the FN coverage region into restricted and non-restricted coverage regions. Then, it coordinates the RBs allocation among FNs by reducing RBs for the vehicles in the non-restricted coverage regions. This reduction is carried out by migrating RBs for offloading upstream services so that the overall occupied capacity of FNs is minimized. ERO constructs the minimum priority queue using the occupied capacity of FNs to perform optimal RBs migration between pairs of FNs. The ERO algorithm is evaluated, and simulation results show that the proposed algorithm performs better in terms of throughput, serviceability, availability, and service capability than existing algorithms.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100911"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621242","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":"Resource allocation strategy for vehicular communication networks based on multi-agent deep reinforcement learning","authors":"Zhibin Liu,&nbsp;Yifei Deng","doi":"10.1016/j.vehcom.2025.100895","DOIUrl":"10.1016/j.vehcom.2025.100895","url":null,"abstract":"<div><div>In complex and high-mobility vehicular communication networks, rapidly changing channel conditions, signal interference, and stringent latency requirements of safety services pose significant challenges to existing wireless resource allocation schemes. We propose a novel resource allocation method named AMADRL. It is based on the multi-agent deep reinforcement learning (MADRL) algorithm and incorporates attention mechanisms (AM). This method first improves the traditional MADRL framework by employing two critic networks to estimate the corresponding global and local reward functions, achieving joint optimization of spectrum and power allocation. This optimization balances the individual interests of agents with the collective benefits, meeting the low-latency communication requirements of vehicle-to-vehicle (V2V) links. And this method effectively reduces the interference to the vehicle-to-infrastructure (V2I) links. Building on this foundation, we further integrate AM into the framework. The AM enables the model to selectively focus on critical information, dynamically adjusting resource allocation strategies. Simulation results demonstrate that, compared with random methods and conventional deep reinforcement learning (DRL) methods, the proposed algorithm exhibits superior convergence speed and stability. It effectively meets the communication requirements of different links and significantly improves spectrum efficiency.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100895"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349672","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":"Effective throughput maximization of beamspace MIMO-NOMA with finite blocklength","authors":"Yiyang Zhang ,&nbsp;Yuan Yin ,&nbsp;Jiaheng Wang ,&nbsp;Kang Zheng","doi":"10.1016/j.vehcom.2025.100908","DOIUrl":"10.1016/j.vehcom.2025.100908","url":null,"abstract":"<div><div>Non-orthogonal multiple access (NOMA) has recently been integrated into beamspace multiple-input multiple-output (MIMO) for improved capacity and throughput. To apply the promising beamspace MIMO-NOMA in practical millimeter-wave applications, finite blocklength has to be considered. Therefore, in this article, we consider the effective throughput as the performance metric, which incorporates both the transmission rate and error performance in the finite blocklength regime. For the considered downlink beamspace MIMO-NOMA system, we derive the expression of system effective throughput with given blocklength and decoding error probability. To maximize the system effective throughput and simultaneously provide the quality-of-service (QoS) guarantee of data rate for each user, the transmit precoding and power allocation are optimized. We first provide an effective precoding design to mitigate the inter-beam interference. For power allocation, we apply monotonic optimization to obtain a globally optimal solution, and further develop a low-complexity algorithm based on the principles of convex-concave procedure (CCP). Simulation results show that the proposed schemes achieve higher spectrum and energy efficiency compared to several baseline schemes, including the traditional resource allocation algorithm based on the infinite blocklength assumption, and the existing beamspace MIMO.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100908"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642083","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":"QRCF: A new Q-learning-based routing approach using a smart cylindrical filtering system in flying ad hoc networks","authors":"Amir Masoud Rahmani ,&nbsp;Amir Haider ,&nbsp;Monji Mohamed Zaidi ,&nbsp;Abed Alanazi ,&nbsp;Shtwai Alsubai ,&nbsp;Abdullah Alqahtani ,&nbsp;Mohammad Sadegh Yousefpoor ,&nbsp;Efat Yousefpoor ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.vehcom.2025.100905","DOIUrl":"10.1016/j.vehcom.2025.100905","url":null,"abstract":"<div><div>To ensure reliable data transmission in flying ad hoc networks (FANETs), efficient routing protocols are necessary to establish communication paths in FANETs. Recently, reinforcement learning (RL), particularly Q-learning, has become a promising approach for overcoming challenges faced by traditional routing protocols due to its capacity for autonomous adaptation and self-learning. This study presents a Q-learning-based routing strategy, enhanced by an innovative cylindrical filtering technique, named QRCF in FANETs. In QRCF, the dissemination interval of hello packets is adaptively adjusted based on the connection status of nearby UAVs. Then, this routing process leverages Q-learning to discover reliable and stable routes, using a state set refined by the cylindrical filtering technique to accelerate the search for the optimal path in the network. Afterward, the reward value is computed using metrics such as relative speed, connection time, residual energy, and movement path. Finally, QRCF is deployed in the network simulator 2 (NS2), and its performance is evaluated against three routing schemes, QRF, QFAN, and QTAR. These evaluations are presented based on the number of UAVs and their speed. In general, when changing the number of nodes, QRCF improves energy usage (about 5.01%), data delivery ratio (approximately 1.20%), delay (17.71%), and network longevity (about 3.21%). However, it has a higher overhead (approximately 10.91%) than QRF. Moreover, when changing the speed of UAVs in the network, QRCF improves energy usage (about 4.94%), data delivery ratio (approximately 2.36%), delay (about 17.5%), and network lifetime (approximately 8.75%). However, it increases routing overhead (approximately 15.47%) in comparison with QRF.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100905"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792512","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":"2025-06-01","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":"Computational intelligence-based routing schemes in flying ad-hoc networks (FANETs): A review","authors":"Parisa Khoshvaght ,&nbsp;Jawad Tanveer ,&nbsp;Amir Masoud Rahmani ,&nbsp;May Altulyan ,&nbsp;Yazeed Alkhrijah ,&nbsp;Mohammad Sadegh Yousefpoor ,&nbsp;Efat Yousefpoor ,&nbsp;Mokhtar Mohammadi ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.vehcom.2025.100913","DOIUrl":"10.1016/j.vehcom.2025.100913","url":null,"abstract":"<div><div>Recently, the rapid development of wireless technologies, low-priced equipment, advances in networking protocols, and access to modern communication, electrical, and sensing technologies have led to the evolution of flying ad hoc networks (FANETs). However, the high movement of unmanned aerial vehicles (UAVs) in these networks causes iterated failures of communication links and constant changes in network topology. These features challenge the design of a proper routing protocol in FANETs. Today, computational intelligence (CI) techniques are rapidly developing as a mighty and intelligent computing model. This promising technology can be used to improve various applied areas, especially routing in FANETs. This paper examines and assesses various CI-based routing techniques in FANETs. Accordingly, this paper introduces a classification of CI-based routing protocols for FANETs. This categorization includes three groups: learning system-based routing methods (including artificial neural networks, reinforcement learning, and deep reinforcement learning), fuzzy-based routing schemes, and bio-inspired routing schemes (evolutionary algorithms and swarm intelligence). Subsequently, based on the offered classification, the most recent CI-based routing methods and their key features are outlined. Ultimately, the opportunities and challenges in this area have been mentioned to help researchers familiarize themselves with future research directions in CI-based routing algorithms for FANETs and work toward improving these methods in such networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100913"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725010","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":"2025-06-01","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":"A distributed multi-agent joint optimization algorithm based on CERL and A2C for resource allocation in vehicular networks","authors":"Ming Sun ,&nbsp;Zexu Jiang ,&nbsp;Erhan Dong ,&nbsp;Tianyu Lv","doi":"10.1016/j.vehcom.2025.100919","DOIUrl":"10.1016/j.vehcom.2025.100919","url":null,"abstract":"<div><div>Vehicular networking plays an indispensable role in enhancing road safety and traffic efficiency. Although existing technologies have made significant progress in reusing vehicle-to-infrastructure (V2I) link resources for vehicle-to-vehicle (V2V) links, they still face challenges such as the high dimensionality of the joint action space and unsatisfactory optimization with limited in-vehicle radio resources, variable environments, and uncertainties. Reinforcement learning-based joint algorithms that separately optimize channel allocation and power selection can reduce the dimensionality of the joint action space. However, it is difficult to effectively coordinate channel allocation and power selection, which greatly affects the performance of them. To address these challenges, this paper proposes a distributed multi-agent joint optimization algorithm based on a novel cross-entropy loss-based reinforcement learning (CERL) algorithm and the A2C algorithm for separately optimizing channels and power in vehicular networks. Furthermore, a multi-round stochastic search strategy is presented to optimize the experience pools and coordinate the channel allocation and the power selection for the proposed distributed multi-agent joint optimization algorithm. With the help of the multi-round stochastic search strategy, the proposed distributed multi-agent joint optimization algorithm can significantly improve the optimization performance in resource allocation. To evaluate the performance of the proposed distributed multi-agent joint optimization algorithm in both the V2V link transmission success rate and the V2I link throughput, a comprehensive simulation study is conducted under different channel resource availability scenarios with different sizes of security data. The experimental results demonstrate that our proposed algorithm can significantly improve the V2I link throughput and the V2V link transmission success rate, and outperforms the existing algorithms in terms of radio efficiency. Specifically, under two different channel resource availability scenarios, our proposed algorithm can achieve more than 99.9 % average V2V link transmission success rate and 2.99 Mbps and 2.07 Mbps higher average V2I link throughput than the competitive algorithm D3QN-LS when the security data size ranges from 1 × 1060 Bytes to 8 × 1060 Bytes. The proposed algorithm theoretically provides a new perspective and solution for separately optimizing channels and power in high-dimensional complex dynamic environments of vehicular networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100919"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825647","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":"Secure energy efficiency maximization for mobile jammer-aided UAV communication: Joint power and trajectory optimization","authors":"Jiazheng Lv,&nbsp;Jianhua Cheng,&nbsp;Peng Li,&nbsp;Runze Bai","doi":"10.1016/j.vehcom.2025.100910","DOIUrl":"10.1016/j.vehcom.2025.100910","url":null,"abstract":"<div><div>This paper considers a mobile jammer-aided unmanned aerial vehicle (UAV) relay communication system, where a relay UAV assists information transmission between the source node and the destination node, while a friendly jammer UAV emits an interference signal to the eavesdropper to suppress its eavesdropping behavior. The secure energy efficiency (SEE) maximization problem is studied. The objective is to maximize the SEE via jointly optimizing power and UAVs' trajectories. The formulated problem is non-convex and subject to information-causality constraints, power constraints, and mobility constraints, which cannot be solved directly by convex optimization tools. To solve the problem, the block coordinate descent method is applied to decouple the original problem into four sub-problems. Then, an efficient iterative algorithm is proposed to address the non-convex problem through the successive convex approximation technique. Additionally, Dinkelbach's algorithm is employed to handle the fractional programming problem, thereby obtaining an approximate solution with guaranteed convergence. Different schemes are evaluated to validate the effectiveness of the proposed design. The simulation results show that the proposed design can improve SEE effectively compared with other schemes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100910"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643796","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}