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The combination of energy harvesting (EH), cognitive radio (CR), andnon-orthogonal multiple access (NOMA) is a promising solution to improve energyefficiency and spectral efficiency of the upcoming beyond fifth generationnetwork (B5G), especially for support the wireless sensor communications inInternet of things (IoT) system. However, how to realize intelligent frequency,time, and energy resource allocation to support better performances is animportant problem to be solved. In this paper, we study joint spectrum, energy,and time resource management for the EH-CR-NOMA IoT systems. Our goal is tominimize the number of data packets losses for all secondary sensing users(SSU), while satisfying the constraints on the maximum charging batterycapacity, maximum transmitting power, maximum buffer capacity, and minimum datarate of primary users (PU) and SSUs. Due to the non-convexity of thisoptimization problem and the stochastic nature of the wireless environment, wepropose a distributed multidimensional resource management algorithm based ondeep reinforcement learning (DRL). Considering the continuity of the resourcesto be managed, the deep deterministic policy gradient (DDPG) algorithm isadopted, based on which each agent (SSU) can manage its own multidimensionalresources without collaboration. In addition, a simplified but practical actionadjuster (AA) is introduced for improving the training efficiency and batteryperformance protection. The provided results show that the convergence speed ofthe proposed algorithm is about 4 times faster than that of DDPG, and theaverage number of packet losses (ANPL) is about 8 times lower than that of thegreedy algorithm.

Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications