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Unmanned Aerial Vehicle (UAV) has become one of the most significant component in future wireless networks since its on-demand and cost-effective deployment. Meanwhile, Device-to-Device (D2D) communications with the nature of providing proximity-based service to improve network performance has emerged as an important feature in 5th generation cellular networks. However, the explosive growth of smart devices and bandwidth-hungry applications cause enormous energy consumption. Energy Harvesting (EH) as a potential solution of improving energy efficiency has shown great importance. Consequently, in this paper, we investigate the resource allocation problem in UAV-aided EH-powered D2D Cellular Networks (UAV-EH-DCNs). Our objective is to maximize the energy efficiency while guaranteeing the satisfaction of ground users (GUs). Owning to the non-convexity of the problem, we formulate the problem as a Markov decision process. Afterward, Deep Deterministic Policy Gradient (DDPG) is proposed to find the optimal strategy. Additionally, Long Short-Term Memory (LSTM) network is employed to facilitate the convergence speed by extracting the previous information of GUs satisfaction to determine the current resource allocation strategy. Numerical results adduce the validity of the proposed DDPG+LSTM algorithm as compared to the DDPG and deep Q-learning algorithms. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm. [ABSTRACT FROM AUTHOR]