论文标题
基于排的C-V2X系统中的渠道分配和功率分配的多代理增强学习
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems
论文作者
论文摘要
我们考虑了在隔板的蜂窝车辆到所有物品（C-V2X）系统中的联合通道分配和电力分配的问题，在这些系统中，多个车辆到网络（V2N）上链路上链路共享与多个车辆对车辆（V2V）平台的时间频资源，可启用连接和自动的车辆近距离旅行。由于在车辆环境中使用高用户移动性的性质，在具有大量用户的C-V2X系统中，依赖全球渠道信息的传统集中优化方法可能不可行。利用多机构增强学习（RL）方法，我们提出了分布式资源分配（RA）算法来克服这一挑战。具体来说，我们将RA问题建模为多代理系统。仅基于本地频道信息，每个排领导者充当代理，共同相互交互，因此选择了子频段和功率水平的最佳组合来传输其信号。为此，我们利用双重深Q学习算法在同时最大化V2N链接的总和率的目标下共同训练代理，并满足所需延迟限制的每个V2V链接的数据包交付概率。仿真结果表明，与众所周知的详尽搜索算法相比，我们提出的基于RL的算法提供了紧密的性能。
We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.
