基于学习的超可靠深度增强智能下载链路调度,用于5G新无线电车辆到基础设施场景
Jizhe Wang1, Yuanbing Zheng1, Jian Wang1
1State Grid Chongqing Information and Telecommunication Company, Chongqing 400012, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种双深Q学习算法,用于优化5G网络中的车辆到基础设施 (V2I) 通信. 新方法提高了传输自动驾驶道路安全关键信息的可靠性和准确性.
科学领域:
- 电信工程 电信工程 电信工程
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 第五代移动通信技术 (5G) 要求车辆与基础设施 (V2I) 连接具有更高的可靠性和效率.
- 新的无线电车到基础设施 (NR-V2I) 系统利用链接适应 (LA) 来管理动态的V2I环境,旨在提高信息传输效率.
- 在V2I通信中的挑战包括多普勒转移和快速时间变化的通道,这降低了传输的可靠性和准确性.
研究的目的:
- 提出基于双深Q学习 (DDQL) 的LA调度算法,以优化自动驾驶汽车V2I通信中的调制和编码方案 (MCS).
- 提高V2I信息传输的可靠性和准确性,特别是道路安全数据,在复杂和动态的通道条件下.
主要方法:
- 该研究提出了一种双深Q学习 (DDQL) 算法,集成深度神经网络 (DNN) 和双Q网络 (DDQN),以优化MCS.
- 该算法解决了Q网络学习的传统深度Q学习 (DQL) 中的高估问题.
- 空间分区复杂化 (SDM) 与MCS一起用于V2I通信中的调度.
主要成果:
- 拟议的DDQL算法证明了适应于具有变化车辆速度的复杂V2I通道环境的适应性.
- 该算法有效地选择最佳的调度方案,使用MCS组合进行V2I道路信息传输.
- 集成SDM显著提高了传输道路安全信息的准确性.
结论:
- 开发的DDQL算法提高了自动驾驶的V2I通信可靠性和效率.
- 优化的MCS和SDM有助于准确传输道路安全信息,促进合作驾驶.
- 该方法有效地减轻了V2I场景中多普勒转移和时间变化的通道所带来的挑战.
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