DDPG-based Sum Rate Optimization for Opportunistic Backscatter NOMA Networks

In today's world of burgeoning IoT and 6G communications, supporting low-powered devices is crucial to fully capitalizing on the Next Generation Internet of Things (NG-IoT) revolution. The proliferation of these devices will unlock unprecedented opportunities for energy efficiency, sustainability, and ubiquitous connectivity. This paper investigates the sum rate optimization of a quality-of-service (QoS)-aware EH-enabled passive IoT device in a cognitive radio inspired non-orthogonal multiple access (CR-NOMA)-assisted backscatter communication network. Our goal is to optimize the sum rate of a secondary passive IoT device while guaranteeing the QoS requirements of the scheduled primary device. The deep deterministic policy gradient (DDPG) algorithm is employed to dynamically adjust the reflection coefficient of the backscatter node, yielding optimal performance. Our results demonstrate significant improvements in the sum rate, highlighting the importance of incorporating advanced machine learning (ML) techniques into IoT and wireless communication domains to address critical challenges and enhance the overall performance of NG-IoT networks.