Energy Efficiency Maximization for CR-NOMA based Smart Grid Communication Network

Managing massive data flows effectively and resolving spectrum shortages are two challenges that Smart Grid Communication Networks (SGCN) must overcome. To address these problems, we provide a combined optimization approach that makes use of Cognitive Radio (CR) and Non-Orthogonal Multiple Access (NOMA) technologies. Our work focuses on using user pairing (UP) and power allocation (PA) techniques to maximize energy efficiency (EE) in SGCN, particularly within Neighbourhood Area Networks (NANs). We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting. This problem is NP-hard, nonlinear, and nonconvex by nature. To address the computational complexity of the problem, we use the Block Coordinate Descent (BCD) method, which breaks the problem into UP and PA subproblems. Initially, we proposed the Zebra-Optimization User Pairing (ZOUP) algorithm to tackle the UP problem, which outperforms both Orthogonal Multiple Access (OMA) and nonoptimized NOMA (UPWO) by 78.8% and 13.6%, respectively, at a SNR of 15 dB. Based on the ZOUP pairs, we subsequently proposed the PA approach, i.e., ZOUPPA, which significantly outperforms UPWO and ZOUP by 53.2% and 25.4%, respectively, at an SNR of 15 dB. A detailed analysis of key parameters, including varying SNRs, power allocation constants, path loss exponents, user density, channel availability, and coverage radius, underscores the superiority of our approach. By facilitating the effective use of communication resources in SGCN, our research opens the door to more intelligent and energy-efficient grid systems. Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.