A deep convolutional-LSTM neural network for signal detection of downlink NOMA system

Abstract

Non-orthogonal multiple access (NOMA) techniques have drawn much attention for massive connectivity, heterogeneous data traffic with ultra-low latency requirements, and ultra-high bandwidth efficiency for the future generation of wireless communication systems. This paper suggests a deep convolutional-long short-term memory (ConvNet-LSTM) aided receiver for detecting signals in the downlink NOMA system. The proposed technique implicitly estimates channel state information and recovers the transmitted symbol directly. The Bayesian approach optimizes the hyperparameters for the deep ConvNet-LSTM. The proposed learning approach is significantly more efficient than the conventional signal detection method, successive interference cancellation (SIC)-based minimum mean square error (MMSE) technique, other deep learning (DL)-based approaches, long short-term memory (LSTM), and convolutional neural networks (CNNs). It simultaneously explores the characteristics of both spatial and temporal features of the received signal for increasing the detection probability of the system. The simulations discuss the advantages of the deep ConvNet-LSTM-based receiver design for downlink NOMA communication systems in terms of bit error rate (BER), achievable rate, and mean squared error (MSE). Furthermore, the robustness of the proposed deep ConvNet-LSTM approach is evaluated for its superiority to other techniques.