Intelligent NOMA-based spectrum sharing in cognitive radio networks: relay, sensing, hybrid, and deep learning frameworks

This work analyzes four novel NOMA-based optimization frameworks: (1) relay-assisted NOMA with DF and AF techniques, showing significant outage improvement over OMA (DF superior); (2) Sensing-NOMA (SNOMA) integrating spectrum sensing with user clustering and power allocation to maximize secondary throughput while protecting primary users; (3) Hybrid NOMA (HNOMA) game-theoretic scheme enabling autonomous coalition formation and dynamic switching between NOMA and OMA modes via preference relations and sequential games, further enhanced by an energy-efficient power allocation game; and (4) deep learning-based NOMA performing signal detection and fair power allocation without explicit channel state information, outperforming conventional SIC in detection accuracy and fairness. Extensive simulations validate all four schemes over existing OMA/NOMA baselines in outage probability, throughput, SE, EE, and fairness, leading to the conclusion that integrating NOMA with relaying, sensing, game theory, and provides a robust, scalable solution for future CRNs, while open challenges and research directions are also highlighted.