E-healthcare system based on wireless body area network (WBAN) promises to produce potential benefits in health-care industry. A major issue of such an on-body networked system is the energy efficiency, that is, how to improve the reliability and effectiveness of physiological data transmission with the energy constraints of tiny wireless sensors. Motivated by this, we consider an individual WBAN scenario, focusing on finding an adaptive time slot allocation and power control scheme to maximize the average energy efficiency for implementing the task of health monitoring. We formulate the maximization problem with latency and sensors’ energy budget constraints as a markov decision process (MDP). As a solution, we propose a deep reinforcement learning-based scheme to make a sequence decision for the MDP, which jointly optimizes power control and slot allocation. Simulation results show that the proposed scheme is energy efficient and has a good convergence.

基于无线人体局域网（WBAN）的电子医疗系统有望在医疗保健行业中产生潜在的收益。这种人体联网系统的主要问题是能量效率，也就是说，如何在微型无线传感器的能量约束下提高生理数据传输的可靠性和有效性。因此，我们考虑了一个单独的WBAN方案，重点是找到一种自适应时隙分配和功率控制方案，以最大程度地提高平均能量效率，以执行健康监控任务。我们将具有延迟和传感器能量预算约束的最大化问题公式化为马尔可夫决策过程（MDP）。作为解决方案，我们提出了一种基于深度强化学习的方案来为MDP做出序列决策，共同优化功率控制和插槽分配。仿真结果表明，该方案具有较高的能效，并且具有良好的收敛性。