Hi, I'm Derek

This paper looks into optimal algorithms for user pairing and triplet grouping in Non-Orthogonal Multiple Access (NOMA) wireless systems, aiming to maximize total utility by unifying channel characteristics with semantic relevance. As 6G wireless systems move toward task-oriented communication, it becomes increasingly important to consider the meaning and importance of user data in resource allocation. We evaluate several existing algorithms including brute force, Hungarian, and greedy approaches under a utility model that incorporates semantic value. Our study show that many traditional algorithms, designed without semantics in mind, perform suboptimally in this setting. Therefore, we propose a greedy algorithm called Semantic Greedy NOMA (SG-NOMA) that considers both channel diversity and semantic value, and demonstrate through simulations that it closely approximates brute force performance with significantly lower complexity. These findings highlight the importance of integrating semantic considerations into user grouping strategies for 6G wireless NOMA deployments.