Distributed Power Controller of Massive Wireless Body Area Networks based on Deep Reinforcement Learning

Abstract

Wireless body area network (WBAN) is encountering a tough challenge in terms of energy efficiency due to multiple realistic factors like increasing scale of network environment, emerging demand of healthcare applications and limited manufacturing technique of sensors. In this work, we address the energy saving issue of WBAN. We consider a layered network framework and hybrid channels with multiple in vivo medium. A distributed power controller is developed based on deep Q-learning algorithm to mitigate the affection of inter-network interference. The proposed power controller utilizes distributed coordinators to learn from WBAN environment and optimize the transmitting power of sensors in the communication. Simulation results demonstrate that our power controller achieves higher performance of energy efficiency compared with two baseline power controllers. Simulation results also demonstrate that proper configuration of proposed power controller of coordinators can significantly achieve the performance gain with the increase of network scale.