用联邦强化学习让车联网车辆智能选信道，提升通信可靠性。
Joint Channel Selection using FedDRL in V2X
- 基于联邦PPO算法，车辆共享经验优化信道选择策略。
- 显著降低信道切换次数，提升通信可靠性与传输效率。
- 适合研究车联网协同决策与分布式学习的开发者参考。
车联网（V2X）技术通过车辆、设备与基础设施间的互联，提升了道路安全、交通效率和驾驶辅助能力。该技术得益于机器学习，实现实时数据分析、更优决策和交通预测。本文研究联合信道选择问题：不同技术的车辆需从多个接入点（AP）中选择一个或多个进行消息传输。车辆需基于自身位置、速度、历史通信的信干噪比（SINR）以及道路类型等信息，学习最优信道选择策略。我们提出一种基于联邦深度强化学习（FedDRL）的方法，采用联邦近端策略优化（FedPPO）算法，使各车辆能共享经验并协同优化。实验表明，该方法在保持通信可靠性的同时，有效降低了传输成本与信道切换频率。通过真实场景仿真验证了方案的高效性，凸显了FedDRL在推进V2X技术方面的潜力。
原文摘要 · Abstract (English)
Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity enhances road safety, transportation efficiency, and driver assistance systems. V2X benefits from Machine Learning, enabling real-time data analysis, better decision-making, and improved traffic predictions, making transportation safer and more efficient. In this paper, we study the problem of joint channel selection, where vehicles with different technologies choose one or more Access Points (APs) to transmit messages in a network. In this problem, vehicles must learn a strategy for channel selection, based on observations that incorporate vehicles' information (position and speed), network and communication data (Signal-to-Interference-plus-Noise Ratio from past communications), and environmental data (road type). We propose an approach based on Federated Deep Reinforcement Learning (FedDRL), which enables each vehicle to benefit from other vehicles' experiences. Specifically, we apply the federated Proximal Policy Optimization (FedPPO) algorithm to this task. We show that this method improves communication reliability while minimizing transmission costs and channel switches. The efficiency of the proposed solution is assessed via realistic simulations, highlighting the potential of FedDRL to advance V2X technology.
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