Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems
Mohammad Farzanullah, Hung V. Vu, Tho Le-Ngoc

TL;DR
This paper introduces a reinforcement learning-based resource allocation method for factory automation robots, enhancing reliability through multi-connectivity and distributed decision-making.
Contribution
It develops a distributed multi-agent reinforcement learning algorithm for joint user association and resource allocation in multi-connectivity URLLC systems.
Findings
Approaches centralized performance in reliability metrics.
Significantly outperforms single-connectivity schemes.
Demonstrates effectiveness in simulated factory scenarios.
Abstract
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…
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Taxonomy
TopicsSmart Grid Security and Resilience
