Sum Rate Maximization in IoT Networks With Diversity-Enhanced Energy Harvesting: A DRL-Guided Approach

In the rapidly evolving landscape of advanced wireless networks, self-sustainable Internet-of-things (IoT) networks become pivotal, necessitating to seamlessly accommodate additional resource-limited devices into the existing wireless infrastructures. To this end, this paper considers an IoT scenario with a wireless-powered communication network (WPCN) where a resource-constrained secondary node (SN) with energy harvesting (EH) capabilities harvests energy from the ambient radio-frequency (RF) signals to meet its energy requirements. Notably, we introduce RF-EH diversity-combining techniques, such as equal gain combining (EGC), maximum ratio combining (MRC), and selection combining (SC) tailored for linear EH models. To address the spectrum scarcity, the SN employs a quality of service (QoS)–aware non-orthogonal multiple access (NOMA) scheme to opportunistically transmit data within the uplink transmissions of the primary devices (PDs) operating around. Aiming to maximize the sum rate of the SN, we jointly optimize the EH time and transmit power of the SN using deep reinforcement learning (DRL). Specifically, we implement a set of DRL and non-DRL algorithms to investigate their robustness in diverse RF-EH diversity-combining environment settings. Simulation results demonstrate the influence of diversity combining techniques on the sum rate performance of the SN, providing valuable insights into their role in optimizing SN performance under dynamic EH environments.