Examination of Deep Learning based NOMA System Considering Node Mobility and Imperfect CSI

Abstract

This paper examines the efficiency of a downlink non-orthogonal multiple access (NOMA) system by using a deep learning (DL)-based stacked long short-term memory (S-LSTM) scheme. The vehicle-to-vehicle (V2V) channel is considered to be time-selective as a result of node mobility and the presence of imprecise channel state information (CSI). The use of the fifth generation (5G) tapped delay line type C (TDL-C) independent and identically distributed (IID) fading channel models allows for the production of channel taps that properly replicate the Nakagami-m fading wireless channel. The paper examines the outage probability (OP) and symbol error rate (SER) of both traditional and suggested channel estimators. It analyzes these metrics under various fading parameters, pilot symbols (PS), learning rate (LR), and batch size. The training of deep neural network (DNN) models is performed using the Adam optimizer. Enhancing the signal-to-noise ratio (SNR) may decrease the SER which results in the enhanced identification of the downlink channel in NOMA cell-based systems. Reducing the LR has a positive effect on the SER, validating the analytical findings that indicate greater changes in DNN weights and larger validation mistakes when the LR is raised. Nevertheless, this benefit is accompanied by the drawback of more frequent updates, resulting in a delay in the model’s convergence.