论文标题
工厂自动化系统中基于增强学习的多连通性资源分配
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems
论文作者
论文摘要
我们建议基于多连通性和增强学习的移动机器人超可信和低潜伏期通信（URLLC）的联合用户协会，渠道分配和电力分配。移动机器人需要定期从中央引导系统中控制消息。我们使用两相通信方案，机器人可以形成多个群集。集群中的机器人彼此近距离，并且可以具有可靠的设备到设备（D2D）通信。在第一阶段，APS将群集的合并有效载荷传输到延迟约束中的群集领导者。集群领导者将此消息广播给了第二阶段的成员。我们为I阶段的联合用户协会和资源分配（RA）开发了分布式的多代理增强学习（MARL）算法。集群领导者使用其本地渠道状态信息（CSI）来决定与子频段和功率级别连接的APS。群集领导者利用多连通性连接到多个AP来提高其可靠性。目的是最大化所有机器人成功的有效载荷交付概率。说明性仿真结果表明，与单连接性相比，所提出的方案可以接近集中式算法的性能，并提供可靠性的可靠性（当群集领导者能够连接到1 AP时）。
We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Device-to-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).
