Distributed congestion control (DCC) improves system performance by lowering channel congestion in vehicular environments with high vehicle density. The 3rd Generation Partnership Project standard defines the related metrics of channel busy ratio (CBR) and introduces possible rate and power control mechanisms to mitigate channel congestion in cellular vehicle-to-everything (C-V2X) sidelink. However, the DCC of C-V2X is not sufficiently specified to implement these controls. In this letter, we propose a novel DCC algorithm based on deep reinforcement learning (DRL) to improve congestion control performance in C-V2X sidelink. The proposed algorithm allows the DRL agent to observe a CBR state and select the packet transmission rate that can maximize the reward of packet delivery rate (PDR) while maintaining higher channel utilization. Simulation results show that the proposed algorithm provides performance gain in terms of PDR and sidelink throughput compared with the existing DCC method.

中文翻译：

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蜂窝 V2X 网络中基于深度强化学习的分布式拥塞控制


分布式拥塞控制 (DCC) 通过减少车辆密度高的车辆环境中的通道拥塞来提高系统性能。第三代合作伙伴项目标准定义了信道繁忙率 (CBR) 的相关指标，并引入了可能的速率和功率控制机制，以缓解蜂窝车联网 (C-V2X) 侧链路中的信道拥塞。然而，C-V2X 的 DCC 没有得到充分的规范来实现这些控制。在这封信中，我们提出了一种基于深度强化学习（DRL）的新型 DCC 算法，以提高 C-V2X 侧链路的拥塞控制性能。所提出的算法允许 DRL 代理观察 CBR 状态并选择可以最大化数据包传输率 (PDR) 回报的数据包传输速率，同时保持较高的信道利用率。仿真结果表明，与现有的 DCC 方法相比，该算法在 PDR 和侧链路吞吐量方面提供了性能增益。