Deep Reinforcement Learning-Based Resource Scheduling Strategy for Reliability-Oriented Wireless Body Area Networks

Reliability is a critical factor in designing of wireless body area networks. In this letter, we propose a resource scheduling strategy and solving an optimization problem to maximize the reliability of the transmission of emergency-critical sensory data. We jointly consider transmission mode, relay selection, time slot allocation, and transmit power of each body sensor and formulating the scheduling problem to be a Markov decision process. In this strategy, the scheduling decision is made by each body sensor that do not have complete and global network information. Owning to the formulated problem is nonconvex and the high computation complexity, we propose a deep reinforcement learning algorithm to solve the problem. Numerical results reveal that the proposed strategy is capacity of guaranteeing the reliability of transmission with an acceptable convergence speed.