Vehicular Communications最新文献_第5页

Evaluation of multichannel operation mechanisms for vehicular networks 车辆网络多通道运行机制评价

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-12-01 Epub Date: 2025-10-07 DOI: 10.1016/j.vehcom.2025.100978

Felipe García Vidal, Esteban Egea López, José Santa Lozano

Upon the massive deployment of Cooperative Intelligent Transportation Systems (C-ITS), services developed for connected vehicles need an efficient, reliable and predictable network access to assure proper operation. European Telecommunications Standards Institute (ETSI) Release 2 services cannot be deployed using a single control channel, and require the management of multiple channels used simultaneously by applications. Because of this, ETSI defined a mechanism called Multi-Channel Operation (MCO). Two simple MCO load allocation mechanisms have been recommended, load balancing and sequential filling, but they have not been evaluated in detail until now. In this paper, these two mechanisms, as well as their congestion control variants, are evaluated. An open-source simulation framework has been implemented for such a work, which is open to future extensions. Then, through multiple evaluations following the ETSI simulation setup, we discuss the behavior of these mechanisms under scenarios with a highly congested medium, employing different traffic loads and vehicular densities. Our results show that MCO improvement is limited under high-load conditions, by saturation of channels before switching to a new one (sequential filling) and synchronization of channel assignment among vehicles (load balancing), and the introduction of a simple reactive congestion control does not improve their performance. The main limitations are examined, and recommendations are provided to guide the evolution of these mechanisms.

随着协作式智能交通系统（C-ITS）的大规模部署，为联网车辆开发的服务需要一个高效、可靠和可预测的网络接入，以确保正常运行。欧洲电信标准协会（ETSI）第2版业务不能使用单个控制通道进行部署，而需要管理应用程序同时使用的多个通道。因此，ETSI定义了一种称为多通道操作（MCO）的机制。推荐了两种简单的MCO负载分配机制：负载平衡和顺序填充，但直到现在还没有对它们进行详细的评估。本文对这两种机制及其拥塞控制变体进行了评估。已经为这样的工作实现了一个开源模拟框架，它对未来的扩展是开放的。然后，通过ETSI模拟设置后的多次评估，我们讨论了这些机制在使用不同交通负载和车辆密度的高度拥挤介质下的行为。我们的研究结果表明，在高负载条件下，由于通道在切换到新通道之前已经饱和（顺序填充）和车辆之间通道分配的同步（负载平衡），MCO的改进是有限的，并且引入简单的反应性拥塞控制并不能提高它们的性能。研究了主要的局限性，并提出了指导这些机制发展的建议。

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引用次数: 0

Secure quantum aggregate signature scheme for vehicular ad-hoc networks 车载自组织网络安全量子聚合签名方案

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.vehcom.2025.100974

Sunil Prajapat , Dheeraj Kumar , M. Shamim Hossain

The rapid evolution of smart cities and intelligent transportation systems has led to the widespread deployment of Vehicular Ad Hoc Networks (VANETs), enabling real-time inter-vehicular communication and data sharing related to traffic congestion, vehicle location, and road safety. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, including identity tracing and message forgery. To address these concerns, this paper proposes a quantum aggregate signature scheme that leverages quantum teleportation and untraceable identity mechanisms for secure and privacy-preserving communication in VANETs. The proposed scheme enables the aggregation of multiple signatures into a single, compact signature while preserving the anonymity of vehicular nodes through pseudo-identities and quantum-enhanced privacy techniques. By incorporating quantum teleportation, our scheme ensures quantum-level security for message transmission without directly transferring the secret state. Furthermore, it supports efficient batch verification to authenticate messages from multiple vehicles with minimal computational and communication overhead. The protocol's correctness and security are validated using both Scyther tool-based formal verification and informal analysis, demonstrating strong resistance against existential forgery, impersonation, and traceability attacks. Compared to existing schemes, our approach reduces computational time by 0.053 ms and communication overhead by 778 bytes, making it scalable, efficient, and highly applicable for real-world VANET deployments.

智能城市和智能交通系统的快速发展导致了车辆自组织网络（vanet）的广泛部署，实现了与交通拥堵、车辆位置和道路安全相关的实时车际通信和数据共享。然而，vanet的开放和动态特性使其容易受到各种安全威胁，包括身份跟踪和消息伪造。为了解决这些问题，本文提出了一种量子聚合签名方案，该方案利用量子隐形传态和不可追踪的身份机制来实现vanet中的安全和隐私保护通信。该方案能够将多个签名聚合为一个紧凑的签名，同时通过伪身份和量子增强隐私技术保持车辆节点的匿名性。通过引入量子隐形传态，我们的方案在不直接传递秘密状态的情况下保证了信息传输的量子级安全性。此外，它支持高效的批量验证，以最小的计算和通信开销验证来自多辆车的消息。使用基于Scyther工具的正式验证和非正式分析验证了协议的正确性和安全性，展示了对存在伪造、冒充和可追溯性攻击的强大抵抗力。与现有方案相比，我们的方法减少了0.053 ms的计算时间和778字节的通信开销，使其具有可扩展性，效率高，并且高度适用于实际的VANET部署。

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引用次数: 0

Scalable AI-driven intent-based vehicle-to-everything communications for secure, low-latency, and energy-efficient smart cities 可扩展的人工智能驱动的基于意图的车辆到一切通信，用于安全、低延迟和节能的智能城市

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-12-01 Epub Date: 2025-10-09 DOI: 10.1016/j.vehcom.2025.100973

Wasim Ahmad , Farman Ali , Aitizaz Ali

The Internet of Things (IoT) and vehicle-to-vehicle (V2V) communication technologies are rapidly growing, and a smart, efficient, and scalable communication framework could accomplish the optimum resource allocation and network performance objective. In view of this, although significant progress has been made in terms of V2V communication through IoT, current urban deployments through dense traffic (> 1,500 vehicles / hour experience average packet latencies ≥120 ms and per-packet energy consumptions ≥100 J, precluding life critical safety applications. To address these constraints, we introduce a novel four-layer V2X framework, AI-integrated Intent-Based Networking (AI-IBN), that:

1.
Abstracts high-level safety and flow intents into the active per-link policies by way of a Deep Reinforcement-Learning controller.
2.
Integrates vehicle-to-vehicle(V2V)/vehicle-to-infrastructure (V2I)/vehicle-to-pedestrian (V2P)/vehicle-to-network (V2N) communication in a single intention manager.
3.
Formulates new subproblems that optimize intersection timing and allocate bus-lanes based, online, and with stochastic gradient updates; and
4.
Encrypts all intents in an end-to-end manner with a lightweight and symmetric-key authentication protocol.

In the urban traffic data set (2 km2 grid, 1500 veh / h) generated by Google Colab in Python, AI-IBN decreases the average packet latency from 150 to 50 ms (67%), reduces the energy per message from 150 to 50 J (67%) and increases the packet success rate from 0.65 to 0.93. In a specific intensity Jamming attack (we have disrupted 10% percent of the channel), our security module maintains a success rate of > 0.90, while a baseline is lower than 0.70. These results enable sub-50-ms and sub-50 J collision-avoidance alerts and set the stage for scalable, life-critical V2X safety services in integrated intelligent transport systems.

物联网（IoT）和车对车（V2V）通信技术正在快速发展，智能、高效、可扩展的通信框架可以实现最佳资源分配和网络性能目标。鉴于此，尽管在通过物联网实现V2V通信方面取得了重大进展，但目前在密集交通（1500辆/小时）的城市部署中，平均数据包延迟≥120ms，每数据包能耗≥100j，阻碍了生命关键安全应用。为了解决这些限制，我们引入了一种新的四层V2X框架，即ai集成的基于意图的网络（AI-IBN），即：1。通过深度强化学习控制器将高级安全和流意图抽象到主动的每链路策略中。将车对车(V2V)/车对基础设施(V2I)/车对行人(V2P)/车对网络（V2N）的通信集成在一个单一的意图管理器中。提出了基于随机梯度更新、在线优化交叉口时间和公交车道分配的新子问题；and4。使用轻量级和对称密钥身份验证协议以端到端方式加密所有意图。在Python中的谷歌Colab生成的城市交通数据集（2 km2网格，1500 veh / h）中，AI-IBN将平均数据包延迟从150 ms降低到50 ms(67%)，将每条消息的能量从150 J降低到50 J(67%)，并将数据包成功率从0.65提高到0.93。在特定强度的干扰攻击中（我们已经干扰了10%的信道），我们的安全模块保持了0.90的成功率，而基线低于0.70。这些结果可实现低于50ms和50j的碰撞避免警报，并为集成智能交通系统中可扩展的、关键生命的V2X安全服务奠定了基础。

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引用次数: 0

Optimizing road side units connectivity in intelligent transportation systems with optical network solutions 利用光网络解决方案优化智能交通系统中道路侧单元的连接

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-12-01 Epub Date: 2025-09-29 DOI: 10.1016/j.vehcom.2025.100975

Camilo Anzola-Rojas, Ignacio de Miguel, Noemí Merayo, Juan Carlos Aguado, Rubén M. Lorenzo, Ramón J. Durán Barroso

A fundamental component of Intelligent Transportation Systems (ITS) is connectivity. For connected vehicles to be aware of events occurring nearby, or even far from them, roadside infrastructure is essential. Roadside Units (RSUs) are electronic equipment placed along highways to provide connectivity and share data with vehicles, other RSUs, and networks. Connected vehicles require wireless communication with RSUs; however, depending on the complexity of tasks and the number of users, spectrum resources may be insufficient to handle all required communication between vehicles, RSUs and external networks. Since RSUs are stationary, optical fiber is an ideal technology for interconnecting them and linking them to the Internet and the cloud, providing reliable, high-performance connectivity, with low signal attenuation and high bandwidth. This paper proposes a model for deploying fiber networks to connect RSUs, with a focus on minimizing capital expenditures, including costs for civil works, cables, and devices, which are critical considerations given the large distances involved. Specifically, we consider and compare two established optical network technologies: point-to-point (PtP) and passive optical networks (PON). To support this comparison, we present and test two novel Integer Linear Programming (ILP) formulations: one for PtP and one for PON. Additionally, we introduce a genetic algorithm that improves upon a previously proposed heuristic, achieving near-optimal results comparable to the ILP formulation, while efficiently solving large-scale scenarios. The results show that the optimal choice between PtP and PON depends on the deployment area and density of RSUs.

智能交通系统（ITS）的一个基本组成部分是连通性。为了让联网车辆了解附近甚至远处发生的事件，路边基础设施是必不可少的。路边单元（rsu）是放置在高速公路沿线的电子设备，用于与车辆、其他路边单元和网络提供连接和共享数据。联网车辆需要与rsu进行无线通信；然而，根据任务的复杂性和用户的数量，频谱资源可能不足以处理车辆、rsu和外部网络之间所需的所有通信。由于rsu是固定的，因此光纤是将它们互连并连接到互联网和云的理想技术，提供可靠、高性能的连接，具有低信号衰减和高带宽。本文提出了一个部署光纤网络连接rsu的模型，重点是最小化资本支出，包括土建工程、电缆和设备的成本，这是考虑到所涉及的大距离的关键考虑因素。具体来说，我们考虑并比较了两种已建立的光网络技术：点对点（PtP）和无源光网络（PON）。为了支持这种比较，我们提出并测试了两个新的整数线性规划（ILP）公式：一个用于PtP，一个用于PON。此外，我们引入了一种遗传算法，该算法改进了先前提出的启发式算法，实现了与ILP公式相当的接近最优结果，同时有效地解决了大规模场景。结果表明，PtP和PON的最优选择取决于rsu的部署面积和密度。

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引用次数: 0

RSU-assisted V2X message fusion via PC5 and Uu with actor–critic modeling for autonomous driving under intersection scenario rsu辅助下基于PC5和Uu的V2X信息融合及角色评价模型在十字路口场景下的自动驾驶

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.vehcom.2025.100972

Sawera Aslam, Daud Khan, Sudeb Mondal, KyungHi Chang

Autonomous driving systems rely heavily on effective data fusion from Vehicle-to-Everything (V2X) networks, where accurate decisions depend on integrating diverse messages from multiple communication interfaces. However, current single-interface communication methods, either PC5 or Uu, limit the achievable autonomy level due to insufficient reliability and situational awareness. To address these limitations, this paper proposes an efficient RSU-centered Message-level fusion framework tailored for intersection-based autonomous driving. The proposed approach strategically assigns CAM, CPM, and SPATEM to the PC5 interface, while DENM and MAPEM are assigned to the Uu interface. A confidence-weighted fusion algorithm is implemented at the RSU aligns timestamps, filters inconsistent inputs, and resolves conflicts to generate unified situational awareness messages every 100 ms. The onboard decision-making model employs a CNN–GRU enhanced Actor–Critic network to optimize decisions for intelligent lane changing, collision avoidance, and traffic flow management. Simulation outcomes confirm that the proposed dual-interface fusion significantly enhances performance compared to single-interface systems, improving the packet delivery ratio to 0.75 at 300 m and achieving decision accuracy improvements of approximately 14–25% across key use cases. Consequently, our framework meets the criteria for autonomy sub-level L4-C, providing a robust foundation for advanced intersection-based autonomous driving systems.

自动驾驶系统在很大程度上依赖于来自车联网（V2X）网络的有效数据融合，而在V2X网络中，准确的决策取决于整合来自多个通信接口的各种信息。然而，目前的单接口通信方法，无论是PC5还是Uu，由于可靠性和态势感知能力不足，限制了可实现的自主水平。为了解决这些限制，本文提出了一个高效的以rsu为中心的消息级融合框架，为基于交叉口的自动驾驶量身定制。该方法将CAM、CPM和SPATEM分配到PC5接口，而将DENM和MAPEM分配到Uu接口。在RSU上实现了一种置信度加权融合算法，该算法对时间戳进行对齐，过滤不一致的输入，并解决冲突，以每100毫秒生成统一的态势感知消息。车载决策模型采用CNN-GRU增强型Actor-Critic网络来优化智能变道、避碰和交通流量管理的决策。仿真结果证实，与单接口系统相比，所提出的双接口融合显着提高了性能，在300米处将数据包投递率提高到0.75，并在关键用例中实现了大约14-25%的决策精度提高。因此，我们的框架符合自主子级L4-C的标准，为先进的基于交叉口的自动驾驶系统提供了坚实的基础。

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引用次数: 0

Budget feasible strategy-proof mechanism design for elastic mobile service in vehicle computing 车辆计算弹性移动服务预算可行防策略机制设计

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-12-01 Epub Date: 2025-10-21 DOI: 10.1016/j.vehcom.2025.100982

Xi Liu , Jun Liu

In the context of the Internet of Things, edge computing can no longer fully meet the needs of the diversified task requirements; hence, vehicle computing has been developed. Concerning the new feature of intelligent vehicles (IVs) mobility, a novel elastic mobile service model is proposed for vehicle computing, where vehicle computing dynamically changes computing power according to the demands. This paper considers the elastic mobile service in two communication scenarios. The first is that IVs from different areas quickly move to the area with insufficient computing power, and the second is that IVs move to the specified area by the user. Owing to the heterogeneous costs of IVs, the pricing is based on the auction. Our goal is to maximize the total value, which is the sum of the values of all tasks running on the IVs. The problem is formulated as an integer linear program and proven

N P

-hard. Then, a budget-feasible strategy-proof mechanism is proposed to allocate IVs one by one based on the marginal contribution. We first show that the proposed mechanism achieves strategy-proofness, individual rationality, consumer sovereignty, and budget balance, which drives the system into equilibrium. The approximation ratio of the proposed mechanism is analyzed. The experimental results show that under budget constraints, the proposed mechanism not only approaches the optimal performance in terms of total value but also effectively allocates tasks to provide the elastic mobile service.

在物联网背景下，边缘计算已经不能完全满足多样化的任务需求；因此，车载计算得到了发展。针对智能车辆移动性的新特点，提出了一种新的车辆计算弹性移动服务模型，车辆计算可以根据需求动态改变计算能力。本文考虑了两种通信场景下的弹性移动业务。第一种是来自不同区域的IVs快速移动到计算能力不足的区域，第二种是IVs由用户移动到指定区域。由于IVs成本的异质性，其定价基于拍卖。我们的目标是使总价值最大化，这是在IVs上运行的所有任务值的总和。该问题被表述为一个整数线性规划，并证明了np困难。然后，提出了一种预算可行的策略证明机制，基于边际贡献逐个分配IVs。我们首先证明了所提出的机制实现了策略抗验证性、个人理性、消费者主权和预算平衡，从而推动系统趋于均衡。分析了所提机构的近似比。实验结果表明，在预算约束下，该机制不仅在总价值方面接近最优性能，而且能够有效地分配任务，提供弹性移动服务。

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引用次数: 0

HQA: Hybrid Q-learning and AODV multi-path routing algorithm for Flying Ad-hoc Networks 飞行自组织网络的混合q -学习和AODV多路径路由算法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-06-23 DOI: 10.1016/j.vehcom.2025.100947

Chen Sun, Liang Hou, Suqi Yu, Jian Shu

Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs.

无人机节点之间可靠、高效的数据传输对于无人机群的控制至关重要，并且在很大程度上依赖于飞行自组织网络（fanet）中有效的路由协议。然而，基于q -学习的FANET路由协议正受到广泛关注，面临着两大挑战：1)q -学习的稳定性不足，导致某些场景下路由选择不可靠，丢包率较高；2)在拓扑变化频繁、路径探索空间广阔的空洞区域，q -学习的收敛速度较慢，不能快速适应动态环境变化，从而降低了分组投递率（PDR）。本文提出了一种混合q -学习/AODV （HQA）多路径路由算法，该算法集成了q -学习和AODV协议来解决这些挑战。HQA包括一个用于自适应q -学习/AODV切换的贝叶斯稳定性评估器和一个双更新奖励机制，该机制将可靠的AODV路径集成到q -学习训练中，从而实现快速的空隙恢复和延迟优化路由。实验结果表明HQA优于基线协议：与AODV相比，HQA在非空和空状态下分别将端到端平均延迟降低13.6-23.9%，将PDR提高5.4-9.1%。在PDR方面，它比QMR高出2.2-6.3%，而在网络密度上，它的平均PDR比QMR和AODV分别高出25.6%和53.2%。与通过aodv辅助奖励的独立Q-learning相比，混合设计的收敛速度提高了40%，在动态拓扑变化下保持了可扩展性。这些结果表明，HQA算法能够更快地适应fanet的快速变化，更好地处理空洞区域，为提高fanet的性能和可靠性提供了一种有前途的解决方案。

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引用次数: 0

Hybrid RF/VLC intelligent vehicular communications: A secrecy analysis 混合RF/VLC智能车载通信：保密性分析

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-08-11 DOI: 10.1016/j.vehcom.2025.100964

E. Soleimani-Nasab , S. Coleri

Next generation intelligent transportation systems (ITS) are expected to use visible light communications (VLC) as a complementary technology to the existing radio frequency (RF)-based technologies in vehicle-to-everything (V2X) communication to provide secure and reliable transmission by exploiting the directivity and impermeability of light. Moreover, reconfigurable intelligent surfaces (RIS) are a promising solution to enhance the coverage and reliability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications by modifying the phase, amplitude and polarization of incoming electromagnetic waves. Most previous works assumed double Rayleigh and Rayleigh fading channels for the RF links, with RIS-assisted setup lacking direct links between vehicles, and non-random distributions for vehicle movement in the VLC links. In this paper, we analyze the physical layer security performance of RIS-assisted hybrid RF/VLC links for both V2V and V2I scenarios. We also assume a direct line-of-sight (LoS) link between legitimate vehicles. In the existence of co-channel interference (CCI), an eavesdropper attempts to receive the information. We employ an accurate method to derive an exact expression for the cumulative distribution function (CDF) of RIS-assisted links combined with a direct link. More specifically, we derive closed-form expressions of secrecy outage probability (SOP), average secrecy capacity (ASC), probability of strictly positive secrecy capacity (PSPSC), effective secrecy throughput (EST), and intercept probability (IP). We assume double Nakagami-m fading for the V2V links, Nakagami-m channel for the V2I links, and log-Normal fading and uniform distribution for both longitude separation of Tx and Tx-Rx distance, leading to random path-loss. The correctness of the derivations is verified by using extensive Monte Carlo simulations for both V2V and V2I scenarios.

下一代智能交通系统（ITS）预计将使用可见光通信（VLC）作为现有基于射频（RF）的车辆到一切（V2X）通信技术的补充技术，利用光的指向性和不渗透性提供安全可靠的传输。此外，可重构智能表面（RIS）是一种很有前途的解决方案，可以通过修改入射电磁波的相位、振幅和极化来增强车对车（V2V）和车对基础设施（V2I）通信的覆盖范围和可靠性。大多数先前的工作假设RF链路的双瑞利和瑞利衰落信道，ris辅助设置缺乏车辆之间的直接链接，并且VLC链路中车辆运动的非随机分布。在本文中，我们分析了V2V和V2I场景下ris辅助混合RF/VLC链路的物理层安全性能。我们还假设合法车辆之间有直接视距（LoS）连接。在共信道干扰（CCI）存在的情况下，窃听者试图接收信息。我们采用一种精确的方法推导出ris辅助链接与直接链接相结合的累积分布函数（CDF）的精确表达式。更具体地说，我们导出了保密中断概率（SOP）、平均保密容量（ASC）、严格正保密容量概率（PSPSC）、有效保密吞吐量（EST）和拦截概率（IP）的封闭表达式。我们假设V2V链路为双Nakagami-m衰落，V2I链路为Nakagami-m信道，Tx和Tx- rx距离的经度分离均为对数正态衰落和均匀分布，导致随机路径损失。通过对V2V和V2I场景进行广泛的蒙特卡罗模拟，验证了推导的正确性。

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引用次数: 0

Optimizing end-to-end latency in C-V2X networks: A novel FD-RAN and MEC integration approach 优化C-V2X网络的端到端延迟：一种新的FD-RAN和MEC集成方法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-03 DOI: 10.1016/j.vehcom.2025.100955

Saber F. Mohammed , Pan Zhiwen , Haithm M. Al-Gunid , Zeyad A.H. Qasem

The increasing demand for low-latency services in cellular vehicle-to-everything (C-V2X) communications is crucial for the efficient operation of connected vehicles and autonomous driving systems. As C-V2X networks become integral to modern transportation infrastructure, minimizing end-to-end (E2E) latency remains a significant challenge in ensuring system reliability and effectiveness. To this end, we propose a novel approach that integrates a fully decoupled radio access network (FD-RAN) with multi-access edge computing (MEC) in C-V2X networks, aiming to optimize latency-sensitive applications. We apply a tractable analytical model for the E2E latency that accounts for latency contributions across the radio, backhaul, network, and processing layers. By leveraging FD-RAN's decoupled access and MEC's distributed processing, our approach effectively mitigates latency bottlenecks inherent in conventional RAN architectures. Additionally, we propose a stochastic optimization-based resource allocation method using Lyapunov techniques and Markov decision processes to dynamically manage base station selection and bandwidth allocation, thereby enhancing the system performance. Simulation results demonstrate that FD-RAN with MEC significantly reduces E2E latency compared with conventional RAN architectures, even under high traffic densities, while maintaining high data rates. These findings validate the proposed approach and offer key insights for developing low-latency infrastructures for next-generation V2X applications.

蜂窝车联网（C-V2X）通信对低延迟服务的需求日益增长，这对于联网车辆和自动驾驶系统的高效运行至关重要。随着C-V2X网络成为现代交通基础设施不可或缺的一部分，最小化端到端（E2E）延迟仍然是确保系统可靠性和有效性的重大挑战。为此，我们提出了一种新颖的方法，将完全解耦的无线接入网络（FD-RAN）与C-V2X网络中的多接入边缘计算（MEC）集成在一起，旨在优化对延迟敏感的应用。我们应用了一个易于处理的端到端延迟分析模型，该模型考虑了无线电、回程、网络和处理层的延迟贡献。通过利用FD-RAN的解耦访问和MEC的分布式处理，我们的方法有效地缓解了传统RAN架构固有的延迟瓶颈。此外，我们提出了一种基于随机优化的资源分配方法，利用李雅普诺夫技术和马尔可夫决策过程来动态管理基站选择和带宽分配，从而提高系统性能。仿真结果表明，与传统RAN架构相比，具有MEC的FD-RAN在保持高数据速率的同时，即使在高流量密度下也能显著降低端到端延迟。这些发现验证了所提出的方法，并为开发下一代V2X应用的低延迟基础设施提供了关键见解。

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引用次数: 0

Energy-harvesting relay-assisted STAR-RIS-enhanced vehicular NOMA networks 能量收集中继辅助star - ris增强型车载NOMA网络

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-24 DOI: 10.1016/j.vehcom.2025.100960

Jianghui Liu , Saibing Wang , Baofeng Ji , Ruijuan Zheng , Hao Li , Guoqiang Zheng

The integration of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with non-orthogonal multiple access (NOMA) technology represents an effective approach for enabling massive device connectivity and achieving 360° network coverage. Under the NOMA scheme, the communication performance of weak users can be significantly enhanced, thereby improving user fairness; however, this often comes at the cost of performance degradation for strong users. Therefore, an energy harvesting relay-assisted STAR-RIS enhanced vehicular NOMA network is considered in this paper. Specifically, the paper provides a detailed analysis of the signal-to-noise ratio (SNR) at the near and far vehicles within the STAR-RIS-assisted vehicular NOMA network. The channel gain from the base station to the near vehicle via STAR-RIS and the composite channel gain between the base station and the far vehicle are approximated using Gamma distributions, with the accuracy of these approximations validated through Monte Carlo simulations. Based on the end-to-end SNR and the statistical characteristics of the channels, closed-form approximate expressions for the outage probabilities of near and far vehicles are rigorously derived, where the direct communication link exists between the far vehicle and the base station, while the energy-harvesting relay provides an auxiliary communication for the near vehicle. All analytical results are validated through simulations. The numerical and simulation results show that, without increasing the number of STAR-RIS elements, the outage performance of the near and far vehicles can be effectively controlled by adjusting the power allocation at the base station and the reflection/transmission coefficients of the STAR-RIS elements. This approach promotes fairness among vehicle users. Moreover, comparison with the orthogonal multiple access (OMA) scheme demonstrates that NOMA achieves better fairness between vehicle users and significantly reduces the outage probability for the far vehicle. Additionally, the energy-harvesting relay helps alleviate the negative impact of NOMA on the near vehicle, thereby further enhancing its communication stability.

同时发射和反射可重构智能表面（STAR-RIS）与非正交多址（NOMA）技术的集成代表了实现大规模设备连接和实现360°网络覆盖的有效方法。在NOMA方案下，弱用户的通信性能可以显著增强，从而提高用户公平性；然而，这通常是以强大用户的性能下降为代价的。因此，本文考虑了一种能量收集中继辅助的STAR-RIS增强型车载NOMA网络。具体而言，本文详细分析了star - ris辅助车辆NOMA网络中远近车辆的信噪比（SNR）。利用伽玛分布近似计算了通过STAR-RIS从基站到近车的信道增益以及基站与远车之间的复合信道增益，并通过蒙特卡罗模拟验证了这些近似的准确性。基于端到端信噪比和信道的统计特性，严格推导了远端车辆与基站之间存在直接通信链路，而能量收集中继为近端车辆提供辅助通信的近端车辆中断概率的封闭近似表达式。通过仿真验证了所有分析结果。数值和仿真结果表明，在不增加星- ris单元数量的情况下，通过调整基站功率分配和星- ris单元的反射/透射系数，可以有效地控制近端和远端车辆的中断性能。这种方法促进了车辆使用者之间的公平。此外，与正交多址（OMA）方案的比较表明，NOMA方案在车辆用户之间实现了更好的公平性，显著降低了远端车辆的中断概率。此外，能量收集中继有助于减轻NOMA对附近车辆的负面影响，从而进一步提高其通信稳定性。

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引用次数: 0