A Hybrid Multiple Access Scheme via Deep Learning-Based Detection

Abstract

In this article, we propose an uplink hybrid multiple access scheme (HMAS) in order to support a highly overloaded multiuser system. In HMAS, for a fixed $K$ -orthogonal resources, there are $K$ -near users (NUs) adopting orthogonal frequency division multiple access and $J>K$ far users (FUs) adopting sparse code multiple access for uplink transmission. To improve the performance of HMAS, we propose two deep learning-based detectors via deep neural network (DNN) models, one for NUs symbol detection, and the other for FUs symbol detection. Both DNN models are trained offline via simulated data and-then-applied for online symbol detection. Simulation results demonstrate the effectiveness of HMAS in terms of symbol error rate performance over Rayleigh fading channels. In particular, it shows that the HMAS with DNN-based detections outperforms significantly the one using conventional message passing algorithm and successive interference cancellation-based detection.