In this letter, a learning-aided resource allocation scheme based on the constrained Markov decision process (CMDP) is proposed to improve the average network energy efficiency (EE) with the constrained quality of service (QoS) in the pattern division multiple access (PDMA)-based simultaneous wireless information and power transfer (SWIPT) system. In order to solve the formulated CMDP resource allocation problem, the Lagrange duality is adopted to transform CMDP into an unconstrained Markov decision process (MDP). Due to the instability of the practical system, the Deep Q Network (DQN)-based CMDP scheme is proposed to obtain the optimal solution. The simulation results verify the proposed scheme converges faster than the benchmark in terms of increasing average network EE.

中文翻译：

---

基于模式分多址的 SWIPT 系统的学习辅助资源分配

在这封信中，提出了一种基于约束马尔可夫决策过程 (CMDP) 的学习辅助资源分配方案，以在模式分多址 (PDMA) 中以约束服务质量 (QoS) 提高平均网络能源效率 (EE) ) 的同步无线信息和电力传输 (SWIPT) 系统。为了解决公式化的CMDP资源分配问题，采用拉格朗日对偶将CMDP转化为无约束马尔可夫决策过程（MDP）。由于实际系统的不稳定性，提出了基于深度Q网络（DQN）的CMDP方案以获得最优解。仿真结果验证了所提出的方案在增加平均网络EE方面比基准更快地收敛。