A DEEP LEARNING FRAMEWORK FOR PREDICTING SIGNALS IN OFDM-NOMA WITH VARIOUS ALGORITHMS

Abstract

The non-orthogonal multiple access (NOMA) approaches have increasingly attracted much interest. It has also been a potential method for wireless communication systems beyond the fifth generation (5G). The successive interference cancellation (SIC) procedure in NOMA systems is often carried out at the receiver, where several users are sequentially decoded. The successful detection of prior users will significantly influence the detection accuracy due to the effects of interferences. A deep learning-based NOMA receiver is analyzed to detect signals for multiple users in a single application without determining channels. This paper analyzes deep learning (DL)- based receiver for NOMA signal detection concerning several DL-aided sequence layers-based algorithms and optimizers by training orthogonal frequency division multiplexing (OFDM) symbols. The simulation outcomes illustrate the various DL-based receiver characteristics using the traditional SIC approach. It also demonstrates that the effect of the different DL-based models is more predictable than the SIC approach.