Enhancing Spectral Efficiency in IoT Networks using Deep Deterministic Policy Gradient and Opportunistic NOMA

Amidst the ongoing debate about limited spectral availability, there remains a persistent demand for the development of spectrally efficient self-sustainable network (SSN) models. This paper addresses this challenge by optimizing spectral efficiency (SE) in uplink transmissions for an energy harvesting (EH)-enabled secondary user (SU) that operates opportunistically among multiple primary users (PUs) in an Internet-of-things (IoT) network. The PUs are assumed to employ a rotational time division multiple access (TDMA) scheme for transmissions, where the signals are divided into time slots for each PU to transmit data in a cyclic manner, while the SU uses an opportunistic non-orthogonal multiple access (NOMA) technique to transmit data without interfering with the PU transmissions, such that, at any given time slot, a PU and a SU share the same frequency band simultaneously. The SE of the system is maximized jointly by employing convex optimization and a deep reinforcement learning (DRL) model, specifically the deep deterministic policy gradient (DDPG) algorithm. Simulations demonstrate that the proposed approach significantly improves the SE of the considered IoT network, highlighting its potential for efficient spectrum management in IoT networks. We present a comprehensive SE analysis of the system, which further underscores the robustness and adaptability of our approach in optimizing SE under diverse operational conditions.