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We propose joint user association, channel assignment and power allocationfor mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based onmulti-connectivity and reinforcement learning. The mobile robots requirecontrol messages from the central guidance system at regular intervals. We usea two-phase communication scheme where robots can form multiple clusters. Therobots in a cluster are close to each other and can have reliableDevice-to-Device (D2D) communications. In Phase I, the APs transmit thecombined payload of a cluster to the cluster leader within a latencyconstraint. The cluster leader broadcasts this message to its members in PhaseII. 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 decidethe APs for connection along with the sub-band and power level. The clusterleaders utilize multi-connectivity to connect to multiple APs to increase theirreliability. The objective is to maximize the successful payload deliveryprobability for all robots. Illustrative simulation results indicate that theproposed scheme can approach the performance of the centralized algorithm andoffer a substantial gain in reliability as compared to single-connectivity(when cluster leaders are able to connect to 1 AP).

Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems