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.

由于诸如网络环境规模不断扩大，医疗保健应用的新兴需求以及传感器制造技术有限等多种现实因素，无线人体局域网（WBAN）在能源效率方面正面临着严峻的挑战。在这项工作中，我们解决了WBAN的节能问题。我们考虑了分层的网络框架和具有多种体内培养基的混合通道。基于深度Q学习算法开发了一种分布式电源控制器，以减轻网络间干扰的影响。提出的功率控制器利用分布式协调器从WBAN环境中学习并优化通信中传感器的发射功率。仿真结果表明，与两个基准功率控制器相比，我们的功率控制器可实现更高的能效性能。仿真结果还表明，随着网络规模的扩大，所建议的协调器功率控制器的正确配置可以显着提高性能。