A Game Theory Based Hybrid NOMA for Efficient Resource Optimization in Cognitive Radio Networks

The deployment of Secondary Networks (SNs) gives an energy and cost-saving outcome for service providers. The existing research on SNs is gleaned from capacity enhancement while reducing interference instead of energy-efficient system, which is wilful for green communication. This paper proposes a game theory-based joint energy-efficient optimization of resources in Cognitive Radio Networks (CRNs) consisting SNs and Primary Networks (PNs) through Hybrid Non-Orthogonal Multiple Access (HNOMA). HNOMA provides freedom for users clustering to utilize either NOMA or OMA. The proposed clustering (priority and non-priority) and PA schemes are jointly optimized to enhance the system sum rate. At the same time, clustering and EGPA are used to break SUs into distinct coalitions and allocate efficient powers. EGPA operates in a distributed manner (without communicating with other BSs), where each BS selfishly updates its PA strategies to maximize its utility. The existence, stability, and uniqueness criteria for the proposed schemes are derived and proved. Simulations are used to corroborate analytical outcomes and uphold the success of proposed schemes in terms of optimum enhancing system energy and spectral efficiency over existing ones.