Influence of SINR and noise variance on outage probability for MIMO-NOMA system in 5G and beyond

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission become ineffective. To address this issue, we introduce a unique statistical beamforming technique that eliminates the necessity for channel reciprocity in channel estimation. This new technology eliminates the overhead associated with pilot broadcasts, increasing spectral efficiency. In our work, we have used the characterization of the indefinite quadratic form ratio to get the outage probability in closed form for the MIMO-NOMA network for 5G and beyond. Later on, a genetic algorithm (GA) was employed for the optimization of beamforming weights. Delivering a spectral efficient beamforming solution, providing an exact analytical characterization of outage probability, and an optimization framework based on GA to resolve constrained multiobjective optimization tasks are the key advantages of our proposed method. The simulation result validates the theoretical analysis and shows the supremacy of the proposed method over the classical statistical method.