Improving 5G Networks’ Average Capacity and BER by Using Uncooperative Underlay and Cooperative Interweave Cognitive Radio NOMA and MIMO

One of the most effective methods for increasing the capacity and reducing the bit error rate (BER) of 5G and other next-generation networks is to implement non-orthogonal multiple access, often known as NOMA. The objective of this paper is to propose two innovative methodologies that, when integrated with uncooperative Underlay (UU) and cooperative Interweave (CI) cognitive radio network (CRN), will provide great potential for decreasing BER and increasing average capacity in the downlink (DL) NOMA power domain (PD). Three different network architectures have been suggested to operate on different transmission powers, utilizing 8 × 8, 16 × 16, and 32 × 32 multiple-input multiple-output (MIMO) configurations. MATLAB is used to calculate the proposed model's average capacity and BER. Using the UUCR-NOMA model improves average capacity performance by 74.7, 89, and 95.6%, respectively, and using the CICR-NOMA model improves average capacity performance by 75.1, 89.2, and 95.7%. MIMO boosts average capacity and BER performance substantially. The provided Monte Carlo simulation results, which validate our work, are in agreement with the derived equation.