A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection

Abstract

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.

Sparse Code Multiple Access: Potentials and Challenges

“…It was proven that the LSTM-based framework is very suitable for user activity and data detection of PD-NOMA systems. In [162], a two user PD-NOMA scenario was considered, a significant performance improvement of a deep neural network (DNN) receiver over a conventional SIC is provided. Also, a joint deep learning of the transmitter precoding and the receiver decoding for downlink MIMO-NOMA was investigated in [167].…”

Performance analysis of MIMO‐based CR–NOMA communication systems

“…Lin et al . [37] have investigated a deep learning (DL)‐based MIMO–NOMA downlink scenario where a solution of effective signal detection is proposed by using the CSI. In the proposed scheme, a learning method is developed that automatically analyses the CSI and detects the original transmit sequences.…”

“…Various researchers have explored the simultaneous exploitation of two techniques, among these three, which are CR–MIMO [26–28], CR–NOMA [29–34] and MIMO‐NOMA [35–37], are known for their better spectral efficiency. Moreover, the simultaneous exploitation of all three techniques is suggested in [7]; however, completely unexplored.…”

Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

“…Wang et al [24], used machine learning approaches for signal detection in MIMO systems with BPSK modulation and channel coding. Lin et al [25] introduced a deep-learning signal detection approach for MIMO non-orthogonal multiple access technique with an M -arry phase modulation. Samuel et al [26] and Wang et al [27] used deep learning for signal detection in MIMO systems with binary phase-shift keying modulation.…”