Optimizing User Pairing in NOMA Using K-Medoids Clustering for Enhanced Spectral Efficiency

Abstract

Non-Orthogonal Multiple Access (NOMA) has emerged as a pivotal technology for 5G and beyond wireless networks, which promises the significant enhancements in spectral efficiency through power-domain multiplexing. However, the practical realization of benefits of NOMA critically depends on solution of the complex user pairing problem, which determines which users should be grouped for simultaneous transmission. This paper basically addresses the critical user pairing challenge in NOMA systems by proposing a novel K-Medoids clustering algorithm. The proposed algorithm optimizes user pairing based on a comprehensive feature vector incorporating channel gain magnitude, angular separation, and potential the signal-to-interference-plus-noise ratio (SINR). The pairing problem is formulated as a clustering optimization with a custom distance metric that balances the channel gain differences and spatial characteristics. The simulation results show the proposed method achieves higher spectral efficiency than the exhaustive search, random pairing, and channel gain difference (CGD), and reduces complexity. The proposed algorithm maintains a Jain's index at excellent level. The proposed algorithm demonstrates robustness across varying user densities while maintaining excellent fairness.