Improved End-to-End Wireless Transmission Integrating NOMA and DL-Based Autoencoder

Abstract

The field of wireless communication systems has experienced significant advancements in recent years, leading to the emergence of two promising technologies: non-orthogonal multiple access (NOMA) and deep learning (DL)-based autoencoders (AE). Through power allocation, NOMA enables multiple users to share a single frequency band, while AE can compress and decompress data with high precision. Integrating NOMA and AE enables end-to-end (E2E) transmission with a superior signal-to-noise (SNR) ratio. To further enhance the wireless network's block error rate (BLER) performance, the multiple-input, multiple-output (MIMO) technique is also incorporated into the newly proposed system. With the incorporation of the MIMO signal, the system is abbreviated as the MIMO-NOMA-AE system. The suggested technique for detecting MIMO-NOMA-AE signals has demonstrated a remarkable performance gain in SNR surpassing the traditional successive interference cancellation (SIC)-based NOMA system. The proposed system also performs better than previously utilized deep neural network (DNN)-based SISO-NOMA-AE communication systems. While this method shows potential for future wireless communication systems, further research and testing are necessary to evaluate its practical feasibility and effectiveness.