Spectral efficient resource allocation using bio-inspired algorithm for NOMA networks

Today's wireless networks allocate radio resources to users based on the Orthogonal Multiple Access (OMA) principle. However, as the number of users increases, OMA-based approaches may not meet the stringent emerging requirements, including very high spectral efficiency, very low latency, and massive device connectivity. The Non-Orthogonal Multiple Access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. NOMA is fundamentally different from orthogonal multiple access schemes which provide orthogonal access to the users either in time, frequency, code, or space. In NOMA, each user operates in the same band and at the same time where they are distinguished by their power levels. In this scheme we used superposition coding for NOMA at the transmitter such that the successive interference cancellation (SIC) receiver can separate the users both in the uplink and in the downlink channels. NOMA with Genetic Algorithm (GA) applied to solve the optimization problem improves spectral efficiency by many folds.