Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
TL;DR
This work proposes a feedback-based deep neural network receiver for signal detection in MIMO-NOMA systems that integrates deep learning capabilities with iterative interference cancellation, enabling improved nonlinear signal separation and robustness against noise and inter-user interference.
Abstract
The increasing demand for high data rates and massive connectivity in next-generation wireless systems has led to the adoption of non-orthogonal multiple access (NOMA) techniques. However, conventional successive interference cancellation (SIC)-based receivers suffer from performance degradation due to error propagation and nonlinear interference effects, particularly in multi-user MIMO environments. To address these limitations, this work proposes a feedback-based deep neural network (FDNN) receiver for signal detection in MIMO-NOMA systems. The proposed model integrates deep learning capabilities with iterative interference cancellation, enabling improved nonlinear signal separation and robustness against noise and inter-user interference. A comprehensive simulation framework is developed using Python to evaluate system performance under varying signal-to-noise ratios (SNR) and power allocation conditions. The results demonstrate that the FDNN-based receiver achieves significantly lower bit error rates (BER) compared to traditional SIC methods, especially in scenarios with small power differences between users. This study highlights the effectiveness of deep learning in enhancing receiver design for future wireless communication systems.