Resource Allocation in Cognitive Radio 5G Massive MIMO NOMA Systems

Abstract

Mobile telecommunications systems and services are widely deployed across global markets. The proliferation of frequency bands and high-speed services underscores the challenge of managing limited frequency resources in next-generation wireless communication systems. Cognitive radio technology presents a promising solution to efficiently utilize spectrum resources. Additionally, emerging technologies like massive multiple-input-multiple-output (MIMO) and non-orthogonal multiple access (NOMA) enhance spectrum efficiency in these systems. This paper focuses on cluster head selection, user allocation to clusters, and power allocation within clusters in a cognitive radio network employing massive MIMO and power domain NOMA. Cluster head decisions are influenced by primary user interference considerations. User clustering is based on the correlation coefficient between each user's channel correlation matrix and the desired cluster head's spatial vector. Power allocation is formulated as a non-convex optimization problem, simplified for practical implementation using the CVX toolbox in MATLAB. Furthermore, the paper introduces a modified zero-forcing (ZF) beamforming technique that nulls interference toward primary users in downlink connections, demonstrating improved spectrum efficiency compared to conventional interference thresholding methods, as validated through simulation results.