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Data-driven Adaptive Network Management with Deep Reinforcement Learning

Advanced wireless communication technologies are trending towards large-scale, geographically distributed systems, which consist of thousands of co-existing devices with potentially conflicting application requirements, such as high data delivery ratio and low power consumption. At the same time, devices are challenged by varying environmental conditions, which can also lead to degradation in network performance. In this paper, a network management system for large scale LoRaWAN-based wireless networks is proposed, aiming to balance application awareness and fairness. Application awareness and fairness are incorporated into a novel reward function, and deep reinforcement learning is used to select node parameters to optimise this reward function. Simulated results demonstrate that this approach is an effective way to automatically manage large-scale LoRaWAN networks.