Capacity Enhancement Based on mMIMO for Cognitive Radio NOMA in Future 6G Networks

The next-generation networks of the future 6G can greatly benefit from using non-orthogonal multiple access, or NOMA, to increase system capacity. This study aims to propose a new approach, combined with a cooperative overly cognitive radio network (COCRN), that significantly improves average capacity in the downlink (DL) NOMA power domain (PD), particularly when integrated with a variety of multiple-input multiple-output (MIMO) and Massive MIMO (mMIMO). Conventional DL NOMA, DL NOMA with OCCRN, 8×8, 16×16, and 32×32 MIMO DL OCCR NOMA, and 128×128, 256×256, and 512×512 mMIMO DL OCCR NOMA are the four proposed network designs that can operate on varied transmission powers. The average capacity of the suggested model is determined using MATLAB. The average capacity performance is enhanced by 18% when the OCCR DL NOMA model is used. Increases of 27%, 49%, and 60 % in average capacity performance are achieved when the OCCR DL NOMA is used with 8×8, 16×16, and 32×32 MIMO models, respectively. The average capacity performance is improved by 75.1 %, 89.8%, and 95.9% when the OCCR DL NOMA with 128×128, 256×256, and 512×512 mMIMO models are used for the best users (U6). The Monte Carlo simulation results offered corroborate our study and align with the calculated equation.