Energy-Efficient Power Allocation in Cognitive Radio Networks Using NOMA and LQR Algorithm

Abstract

As new radio access technologies and IoT devices proliferate, the design of 6G networks faces significant challenges related to hardware cost, power consumption, and spectrum scarcity. This study explores the combination of Non-Orthogonal Multiple Access (NOMA) and Cognitive Radio (CR) to enhance spectrum efficiency and system throughput in mobile communication networks. A novel power allocation algorithm using the Linear Quadratic Regulator (LQR) is proposed for CR-NOMA networks, aiming to optimize energy efficiency for secondary users (SUs) while ensuring the quality of service for primary users (PUs). The proposed algorithm dynamically adjusts transmit power based on environmental conditions, offering faster convergence and lower time complexity compared to traditional methods like genetic and FTPC algorithms. Simulation results demonstrate superior energy efficiency and system performance, highlighting the potential of CR-NOMA networks in future communication systems. Future research will address power allocation under non-perfect channel conditions to further refine the model's applicability.