用深度强化学习优化车载网络频谱分配，提升通信效率。
Spectrum Sharing using Deep Reinforcement Learning in Vehicular Networks
- 采用DQN模型自主学习最优频谱分配策略。
- 训练过程中累积奖励持续上升，通信成功率显著提升。
- 适合研究车联网动态资源管理的学者与工程师。
随着连接到车载网络的设备数量呈指数增长，如何在动态的车载环境中高效分配频谱面临越来越多挑战，传统方法已难以应对。车载网络涉及安全关键消息，需实现高效的频谱分配以保障通信顺畅并缓解网络拥塞。为此，本文提出一种基于深度Q网络（DQN）的解决方案，利用其随时间学习最优策略并做出决策的能力。实验结果表明，该模型能有效提升频谱共享效率。深度强化学习在车载网络频谱共享中的应用展现出良好前景，系统具备适应动态通信环境的能力。SARL与MARL模型均表现出较高的车对车（V2V）通信成功率，且强化学习模型的累积奖励随训练进程不断达到峰值。
原文摘要 · Abstract (English)
As the number of devices getting connected to the vehicular network grows exponentially, addressing the numerous challenges of effectively allocating spectrum in dynamic vehicular environment becomes increasingly difficult. Traditional methods may not suffice to tackle this issue. In vehicular networks safety critical messages are involved and it is important to implement an efficient spectrum allocation paradigm for hassle free communication as well as manage the congestion in the network. To tackle this, a Deep Q Network (DQN) model is proposed as a solution, leveraging its ability to learn optimal strategies over time and make decisions. The paper presents a few results and analyses, demonstrating the efficacy of the DQN model in enhancing spectrum sharing efficiency. Deep Reinforcement Learning methods for sharing spectrum in vehicular networks have shown promising outcomes, demonstrating the system's ability to adjust to dynamic communication environments. Both SARL and MARL models have exhibited successful rates of V2V communication, with the cumulative reward of the RL model reaching its maximum as training progresses.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。