A Comparative analysis of Deep Learning-Aided-NOMA and Multi Carrier-NOMA for 5G Networks and beyond

Abstract

This paper presents a comprehensive comparative analysis of deep learning-aided NOMA and multi-carrier NOMA for 5G networks and beyond. We investigate the performance of both techniques in terms of bit error rate, transmit power, signal-to-noise ratio, achievable capacity, outage probability, and data rate in a MATLAB environment. Simulation results of two techniques were evaluated. Furthermore, a comparative analysis identifies that the DL-NOMA exhibits superior performance in terms of power consumption, outage probability, and bit error rate. The results show that DL-NOMA reduces power consumption by 23.4%, outage probability by 27.6%, and improves bit error rate by 56.4%.