Improving NOMA Performance by Application of Autoencoders and Equidistant Power Allocation

Non-orthogonal multiple access (NOMA) has been introduced as a promising scheme to allow for superposition of signals, such as the transmission of multiple services in the same resource block (time and frequency). In this paper, we propose the application of Deep Learning (DL) in an Autoencoder (AE) framework for simultaneous usage in multi-service NOMA transmission. While classical NOMA simultaneously incorporates locally separated user equipments (UEs), we focus on the simultaneous transmission of services within a single UE. Our scheme achieves improved performance compared to classical NOMA schemes and is capable of performing optimal estimation of the superimposed transmit signals. The scheme utilizes an equidistant power allocation scheme. The results show the potential of using DL to enhance the performance of NOMA systems and improve their adaptability and flexibility to different scenarios.