Vehicular Communications最新文献_第6页

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 : 2025-07-24 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

Fault tolerant mission management for UAV under random threat using Markov decision process 基于马尔可夫决策过程的无人机随机威胁容错任务管理

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-22 DOI: 10.1016/j.vehcom.2025.100959

Md Muzakkir Quamar , Ali Nasir , Sami ELFerik

This research introduces a comprehensive framework for mission accomplishment of unmanned aerial vehicles (UAVs) operating in threat-prone zones. Leveraging a Markov Decision Process (MDP), the proposed model ensures mission accomplishment and optimal resource utilization by incorporating UAV state-of-charge, post-fault capabilities, and threat navigation strategies. The framework addresses sensor, actuator, and vision camera faults, enabling dependable operations even under adverse conditions. A key feature of the model is the integration of UAV battery levels to evaluate operational range relative to mission objectives, optimizing task distribution while conserving energy. Additionally, the incorporation of adaptive navigation modes enhances UAV agility and robustness by enabling threat avoidance during operation, closely emulating real-world scenarios. By synthesizing repair protocols, recharging strategies, and stochastic modeling of recurrent goals and threats, the framework offers a holistic solution to improve resilience and mission success in hostile environments. Stochastic dynamic programming ensures the rapid application of precomputed optimal policies during mission execution. A simulation-based case study demonstrates the framework's effectiveness in navigating threats, mitigating faults, and ensuring mission reliability in energy-constrained scenarios.

本文介绍了一种用于无人机在威胁易发区域执行任务的综合框架。利用马尔可夫决策过程（MDP），该模型通过结合无人机状态、故障后能力和威胁导航策略，确保任务完成和最佳资源利用。该框架解决了传感器、执行器和视觉相机故障，即使在恶劣条件下也能实现可靠的操作。该模型的一个关键特征是集成无人机电池水平，以评估相对于任务目标的作战范围，在节约能源的同时优化任务分配。此外，自适应导航模式的结合增强了无人机的敏捷性和鲁棒性，通过在操作过程中实现威胁规避，密切模拟现实世界的场景。通过综合修复协议、充电策略和反复出现的目标和威胁的随机建模，该框架提供了一个整体解决方案，以提高敌对环境下的恢复能力和任务成功率。随机动态规划保证了任务执行过程中预先计算的最优策略的快速应用。基于仿真的案例研究证明了该框架在导航威胁、减轻故障和确保能源受限场景下任务可靠性方面的有效性。

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引用次数: 0

A secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANET 一种安全高效的基于格的VANET条件隐私保护认证协议

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-22 DOI: 10.1016/j.vehcom.2025.100958

Dongxian Shi , Xuwen Nie , Ming Xu , Hongbing Cheng , Muhammad Alam

On the benefit of the prosperous development of the communication techniques and automatic driving, the vehicle ad hoc network (VANET) is becoming more and more commonplace. The information transmitted in the VANETs is exposed in the open wireless communication environment, so it is vulnerable to several types of attacks. To balance the information security and privacy in the VANETs, there are a great number of conditional privacy-preserving authentication protocols proposed. However, only a few of them are resistant to quantum attacks, and these existing quantum-resistant works are unsatisfactory, ether are insecure or suffer from other problems. In this paper, we propose a secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANETs, which can achieve authentication and privacy protection, and a batch verification method is provided to further optimize the performance. Compared with the existing counterparts, our protocol is secure, efficient, and achieving lowest communication overhead. We provide several parameter sets, and the protocol achieves least execution time under some of them. We also show a security proof of the protocol in the random oracle model, based on the assume of inhomogeneous small integer solution problem.

随着通信技术和自动驾驶技术的蓬勃发展，车辆自组织网络（VANET）变得越来越普遍。在VANETs中传输的信息暴露在开放的无线通信环境中，因此容易受到多种类型的攻击。为了平衡vanet中的信息安全和隐私，人们提出了大量的条件隐私保护认证协议。然而，它们中只有少数能够抵抗量子攻击，而且这些现有的抗量子工作并不令人满意，要么是不安全的，要么是存在其他问题。本文提出了一种安全高效的基于格的vanet条件隐私保护认证协议，实现了认证和隐私保护，并提供了批验证方法进一步优化性能。与现有协议相比，我们的协议安全、高效、通信开销最小。我们提供了几个参数集，在其中一些参数集下，协议的执行时间最短。基于非齐次小整数解问题的假设，给出了该协议在随机oracle模型下的安全性证明。

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引用次数: 0

MetaCAN: An optimized adaptive hybrid metaheuristic-based intrusion detection system for CAN bus security MetaCAN：一种基于优化自适应混合元启发式的CAN总线安全入侵检测系统

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-10 DOI: 10.1016/j.vehcom.2025.100956

Kadir Ileri , Abdur Rakib , Soufiene Djahel

The Controller Area Network (CAN) bus is a message-based protocol widely used in modern vehicles to facilitate communication between various Electronic Control Units (ECUs). However, its simplistic design lacks fundamental security measures, making it highly susceptible to cyberattacks. These vulnerabilities pose significant risks to vehicle safety, highlighting the critical need for implementation of effective intrusion detection systems (IDS). Therefore, in this paper, a machine learning based IDS optimized through an adaptive hybrid metaheuristic approach, named MetaCAN, is proposed to secure the CAN bus. MetaCAN leverages the complementary strengths of particle swarm optimization (PSO) for fast convergence and cuckoo search (CS) for robust global search to ensure effective hyperparameter tuning and model optimization. MetaCAN is evaluated using three real-world datasets including Survival Analysis, Car Hacking: Attack & Defense Challenge 2020, and OTIDS. Unlike traditional binary detection systems, MetaCAN offers multi-class attack detection by identifying five distinct attack types including Denial of Service (DoS), fuzzy, masquerade, malfunction, and replay attacks. Moreover, the detection accuracy of the system is enhanced through a feature engineering process that introduces two effective features such as Time Interval and ID Repetition Count. The experimental results show that MetaCAN consistently outperforms existing IDS solutions targeted the same datasets, making it a promising solution for securing the CAN bus in real-world vehicular environments.

控制器局域网（CAN）总线是一种基于消息的协议，广泛应用于现代车辆中，用于促进各种电子控制单元（ecu）之间的通信。然而，其过于简单的设计缺乏基本的安全措施，使其极易受到网络攻击。这些漏洞对车辆安全构成了重大风险，强调了实施有效入侵检测系统（IDS）的迫切需要。因此，本文提出了一种基于机器学习的IDS，通过自适应混合元启发式方法进行优化，称为MetaCAN，以保护CAN总线。MetaCAN利用粒子群优化（PSO）的快速收敛和布谷鸟搜索（CS）的鲁棒全局搜索的互补优势，确保有效的超参数调谐和模型优化。MetaCAN使用三个真实世界的数据集进行评估，包括生存分析，汽车黑客攻击；《国防挑战2020》和OTIDS。与传统的二进制检测系统不同，MetaCAN通过识别五种不同的攻击类型提供多类攻击检测，包括拒绝服务（DoS）、模糊攻击、伪装攻击、故障攻击和重放攻击。此外，通过引入时间间隔和ID重复计数两个有效特征的特征工程过程，提高了系统的检测精度。实验结果表明，MetaCAN始终优于针对相同数据集的现有IDS解决方案，使其成为在实际车辆环境中保护CAN总线的有前途的解决方案。

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引用次数: 0

Energy efficiency optimization for UAV-mounted IRS assisted ISAC systems under statistical CSI 统计CSI下无人机机载IRS辅助ISAC系统的能效优化

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-05 DOI: 10.1016/j.vehcom.2025.100953

Peng Wang , Huizhi Tang , Demin Li , Yihong Zhang , Xuemin Chen

Integrated Sensing and Communication (ISAC) systems are advantageous for enhancing both communication and sensing capabilities, but their performance is significantly impacted by signal blockages in dynamic vehicular environments. An Unmanned Aerial Vehicle (UAV)-mounted Intelligent Reflective Surface (IRS) for air-to-ground communication and sensing can significantly enhance coverage and deployment flexibility. However, the additional power consumption of the UAV-mounted IRS (UIRS) remains a challenge. To mitigate this, we propose a novel UIRS-assisted ISAC system that aims to maximize communication energy efficiency (EE) while meeting sensing quality-of-service (QoS) requirements by optimizing the UAV trajectory, IRS passive beamforming, and base station (BS) active beamforming. Due to the complex and dynamic nature of wireless channels, acquiring Channel State Information (CSI) is challenging, especially with the UAV's mobility and the passive mode of IRS. Therefore, statistical CSI is adopted in the proposed scheme. The optimization problem is reformulated into a tractable form and solved by decomposing it into three subproblems, which include using the Dinkelbach transformation for fractional programming in EE calculation, Successive Convex Approximation (SCA) for UAV trajectory optimization, and Semi-Definite Relaxation (SDR) for both active and passive beamforming designs. An alternating optimization (AO)-based framework iteratively solves all subproblems, with proven algorithm convergence and computational efficiency. Simulation results demonstrate that the proposed UIRS-assisted ISAC system significantly improves both communication and sensing performance compared to benchmark schemes.

集成传感与通信（ISAC）系统有利于提高通信和传感能力，但其性能受到动态车辆环境中信号阻塞的显著影响。用于空对地通信和传感的无人机（UAV）安装的智能反射面（IRS）可以显著提高覆盖范围和部署灵活性。然而，无人机机载IRS （UIRS）的额外功耗仍然是一个挑战。为了缓解这一问题，我们提出了一种新的uirs辅助ISAC系统，该系统旨在通过优化无人机轨迹、IRS无源波束形成和基站（BS）有源波束形成，最大限度地提高通信能效（EE），同时满足感知服务质量（QoS）要求。由于无线信道的复杂性和动态性，获取信道状态信息（CSI）是一个挑战，特别是考虑到无人机的机动性和IRS的被动模式。因此，本方案采用统计CSI。将优化问题重新表述为易于处理的形式，并将其分解为三个子问题来解决，包括在EE计算中使用Dinkelbach变换进行分数规划，在无人机轨迹优化中使用逐次凸逼近（SCA），以及在主动和被动波束形成设计中使用半确定松弛（SDR）。基于交替优化（AO）的框架迭代求解所有子问题，证明了算法的收敛性和计算效率。仿真结果表明，与基准方案相比，所提出的uirs辅助ISAC系统在通信和感知性能方面都有显著提高。

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引用次数: 0

Energy and experimental trust-based task offloading in the domain of connected autonomous vehicles 互联自动驾驶汽车领域中基于能量和实验信任的任务卸载

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-04 DOI: 10.1016/j.vehcom.2025.100954

Sachin Kumar Gupta , Anuradha Banerjee

Task offloading among connected and autonomous vehicles (CAVs) has recently gained much attention. The current literature in this context mostly optimizes only the criterion of energy and latency. Further, issues like connectivity and spontaneous attitude of selflessness have remained unexplored despite their importance and probable contribution to preserving vehicles' energy and reducing overall delay in completing the tasks. Therefore, the key objectives of the present study are maximization of residual energy and percentage of successful offloading, as well as minimization of energy consumption and delay. We have also considered trust, which has two components; efficiency and certainty. Efficiency is defined as the inverse of the estimated time duration required to complete the execution of the current task based on the history of the previous sessions. Certainty is related to the stability of the connection between the server and task off-loader vehicles and the selfless cooperation of the server, as revealed from the history of communication with the off-loader. Experimental results show that our proposed method of offloading tasks based on energy and experiential trust (OTEET) increases the offload success percentage and reduces cost by approximately 40%, which can be considered a significant improvement.

最近，自动驾驶汽车（cav）之间的任务卸载备受关注。目前在这方面的文献大多只优化了能量和潜伏期的标准。此外，连通性和自发的无私态度等问题仍未得到探索，尽管它们对节省车辆能源和减少完成任务的总体延迟具有重要意义和可能的贡献。因此，本研究的关键目标是剩余能量和成功卸载百分比的最大化，以及能量消耗和延迟的最小化。我们还考虑了信任，它有两个组成部分；效率和确定性。效率的定义是根据以前的会话历史完成当前任务所需的估计时间的倒数。从与卸载器的通信历史可以看出，确定性与服务器与任务卸载器之间连接的稳定性以及服务器的无私合作有关。实验结果表明，基于能量和经验信任（OTEET）的任务卸载方法提高了任务卸载成功率，降低了约40%的成本，是一种显著的改进。

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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 : 2025-07-03 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

Dynamic offloading strategy in SAGIN-based emergency VEC: A multi-UAV clustering and collaborative computing approach 基于sagin的应急VEC动态卸载策略：一种多无人机聚类与协同计算方法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-03 DOI: 10.1016/j.vehcom.2025.100952

Zhenzheng Shi, Liang Wang, Yaguang Lin, Anna Cai, Jiamin Fan, Cong Liu

Mobile edge computing (MEC) technology can provide stable and efficient computing services for ground vehicles and users. However, maintaining stable MEC services becomes challenging in scenarios where ground MEC servers are damaged or unavailable, such as in post-disaster or remote areas. To tackle this issue, this paper proposes a novel space-air-ground integrated network (SAGIN) based emergency vehicular edge computing (VEC) framework, leveraging the rapid deployment characteristic of unmanned aerial vehicle (UAV) to provide VEC services for ground vehicles. A distance-based UAV clustering (DUC) algorithm is designed for efficient multi-UAV collaboration, executed by low earth orbit (LEO) satellite with wide coverage. Within each cluster, a task splitting algorithm based on a novel expected computing delay (ECD) metric is performed by the cluster-head UAV (CHU). Focusing on the issue of limited line-of-sight (LoS) range of UAV and computing sustainability during vehicle moving, we propose a dynamic offloading strategy. Simulation results show that the proposed framework enhances UAV utilization by 60% and significantly reduces task process delays across varying scenarios.

移动边缘计算（MEC）技术可以为地面车辆和用户提供稳定高效的计算服务。然而，在地面MEC服务器损坏或不可用的情况下，例如灾后或偏远地区，维持稳定的MEC服务变得具有挑战性。针对这一问题，本文提出了一种新的基于天空地一体化网络（SAGIN）的应急车辆边缘计算（VEC）框架，利用无人机（UAV）的快速部署特性，为地面车辆提供VEC服务。设计了一种基于距离的无人机聚类（DUC）算法，以实现覆盖范围广的近地轨道（LEO）卫星上的多无人机高效协同。在每个簇内，簇头无人机（CHU）基于一种新的期望计算延迟（ECD）度量执行任务分割算法。针对无人机有限视距和车辆移动可持续性计算问题，提出了一种动态卸载策略。仿真结果表明，该框架提高了60%的无人机利用率，显著降低了不同场景下的任务过程延迟。

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引用次数: 0

Multi-objective resource allocation for UAV-assisted air-ground integrated full-duplex OFDMA networks 无人机辅助地空一体化全双工OFDMA网络的多目标资源分配

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-02 DOI: 10.1016/j.vehcom.2025.100951

Tong Wang

In multi-UAV-assisted air-ground integrated in-band full-duplex (IBFD) OFDMA networks, both uplink and downlink performances are critical and must be simultaneously considered. This study addresses effective resource allocation in such networks to maximize the total system uplink and downlink rates by jointly optimizing subcarrier assignment and power control. Given the significant trade-off between uplink and downlink transmissions owing to self-interference in IBFD systems and intercell interference, we formulate the resource allocation problem as a multi-objective optimization problem (MOOP), aiming to jointly maximize the uplink and downlink performances. To achieve Pareto optimal solutions, we employ the weighted Tchebycheff technique to transform the MOOP into a single-objective optimization problem (SOOP) and solve it using Successive Convex Approximation (SCA) within a Block Coordinate Descent (BCD) framework. This approach iteratively optimizes the subcarrier assignment and power control and effectively manages the trade-offs between uplink and downlink rates. The proposed method demonstrates the ability to achieve an efficient balance in resource allocation. Simulation results show that our method can obtain Pareto optimal solutions, demonstrating favorable performance trade-offs and fairness under various interference conditions, thereby improving the overall system performance in multi-UAV-assisted air-ground integrated OFDMA networks.

在多无人机辅助的空地带内全双工（IBFD） OFDMA网络中，上行链路和下行链路的性能至关重要，必须同时考虑。本研究通过联合优化子载波分配和功率控制，解决了在此类网络中有效的资源分配问题，以最大限度地提高系统的总上行和下行速率。考虑到IBFD系统的自干扰和小区间干扰导致上下行传输之间存在显著的权衡，我们将资源分配问题制定为多目标优化问题（MOOP），旨在共同最大化上下行性能。为了实现Pareto最优解，我们采用加权Tchebycheff技术将MOOP转化为单目标优化问题（SOOP），并在块坐标下降（BCD）框架内使用连续凸逼近（SCA）进行求解。该方法迭代优化了子载波分配和功率控制，有效地管理了上行和下行速率之间的权衡。所提出的方法证明了实现资源分配有效平衡的能力。仿真结果表明，该方法可以获得Pareto最优解，在各种干扰条件下表现出良好的性能权衡和公平性，从而提高了多无人机辅助地空一体化OFDMA网络的整体系统性能。

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引用次数: 0

Deep reinforcement learning based migration and execution decisions for multi-hop task offloading in mobile vehicle edge computing 基于深度强化学习的移动车辆多跳任务卸载迁移与执行决策

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-07-02 DOI: 10.1016/j.vehcom.2025.100950

Wenjie Zhou, Tian Zhang, Zekun Lu, Linbo Zhai

As the Internet of Things (IoT) drives the development of Vehicular Edge Computing (VEC), there is a surge in computational demand from emerging in-vehicle applications. Most existing studies do not fully consider the frequent changes in network topology under high mobility of vehicles and the underutilization of idle resources by single-hop offloading. To this end, we propose a task offloading scheme for vehicular edge computing based on multi-hop offloading. The scheme allows task vehicles to offload tasks to service vehicles with excess idle resources outside the communication range, and adapts to dynamic changes in network topology by introducing the concept of neighboring vehicle connection time. This study aims to minimize the delayed energy consumption utility value of the task under the conditions of satisfying the maximum task delay limit, vehicle computational and storage resource constraints. In response to this NP-hard problem, a two-stage reinforcement learning strategy MOCDD (combining Deep Q Network (DQN) and Deep Deterministic Policy Gradient (DDPG)) is proposed to divide the mixed action space into pure discrete and pure continuous action space to determine task migration, executive decision and vehicle transmission power. Simulation results verify the effectiveness of the proposed scheme.

随着物联网（IoT）推动车辆边缘计算（VEC）的发展，新兴车载应用的计算需求激增。现有的研究大多没有充分考虑车辆高机动性下网络拓扑结构的频繁变化和单跳卸载对空闲资源的充分利用。为此，我们提出了一种基于多跳卸载的车辆边缘计算任务卸载方案。该方案允许任务车辆将任务卸载到通信范围外有多余空闲资源的服务车辆上，并通过引入相邻车辆连接时间的概念来适应网络拓扑的动态变化。本研究的目标是在满足最大任务延迟限制、车辆计算和存储资源约束的条件下，使任务的延迟能耗效用值最小化。针对这一NP-hard问题，提出了一种结合深度Q网络（Deep Q Network， DQN）和深度确定性策略梯度（Deep Deterministic Policy Gradient， DDPG）的两阶段强化学习策略MOCDD，将混合动作空间划分为纯离散和纯连续动作空间，以确定任务迁移、执行决策和车辆传输功率。仿真结果验证了该方案的有效性。

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引用次数: 0