Li Zhijuan, Li Guohong, Wu Zhuofei, Zhang Wei, Bazzi Alessandro
School of Computer and Big Data, Heilongjiang University, Harbin 150080, China.
Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, Harbin 150090, China.
Sensors (Basel). 2025 Aug 21;25(16):5209. doi: 10.3390/s25165209.
Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor-Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios.
车对车（V2V）和车对网络（V2N）通信是智能交通系统（ITS）的两个关键组成部分，它们可以通过带内叠加共享频谱资源。V2V通信主要支持交通安全，而V2N主要侧重于信息娱乐和信息交换。在资源受限、动态变化的交通环境中实现可靠的V2V传输以及高速率的V2N服务，对资源分配构成了重大挑战。为了解决这个问题，我们提出了一种新颖的强化学习（RL）框架，称为图注意力网络（GAT）-优势演员评论家（GAT-A2C）。在这个框架中，我们基于V2V链路及其潜在干扰关系构建一个图。每个V2V链路都表示为一个节点，边连接可能相互干扰的节点。GAT在考虑实时移动性和信道变化的同时，捕捉相邻车辆之间的关键干扰模式。由GAT生成的特征与各个链路特征相结合，形成环境状态，然后由RL智能体进行处理，以联合优化V2V和V2N通信的资源块分配和发射功率。仿真结果表明，所提出的方法在各种车辆密度下显著提高了V2N速率和V2V通信成功率。此外，该方法具有很强的可扩展性，使其成为未来在动态交通场景中运行的大规模智能车辆网络的一个有前途的解决方案。
