Analysis of the Fifth Generation NOMA System Using LSTMAlgorithm

This study investigates non-orthogonal multiple access (NOMA) receivers based on deep learning (DL) employing the long-short term memory technique (LSTM) over frequency-flat Rayleigh distributed fading links. The fading links are independently and identically distributed (i.i.d.). When comparing the DL-based NOMA receiver’s system performance to that of the traditional NOMA technique, the DL-based receiver surpasses the conventional successive interference cancellation (SIC)-based NOMA receiver. The simulations are conducted for various values of cyclic prefix (CP) considering the clipping noise (CN) under real-time propagation characteristics. It has been discovered that neither minimum mean square error (MMSE) nor least square error (LSE) can provide precise information on fading channel coe ffi cients. With a signal-to-noise ratio (SNR) value exceeding 14 dB, precision tends to be saturated. On the other hand, DL techniques continue to be e ff ective in channel estimation and detection. Lower learning rates improve system performance, whereas a high learning rate generates rapid changes in the weights of the DL NOMA detector, leading to a very high validation error value.