# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 1 Introduction  
Since the concept of non-orthogonal multiple access (NOMA) transmission was proposed, there have been at least three categories of non-orthogonal multiple access: power-domain NOMA, code-domain NOMA, and hybrid-domain NOMA. In this article, we focus on power-domain NOMA for downlink multiuser multiple-input multiple-output (MIMO) systems on account of its representativeness and typicality of the non-orthogonal multiple access technique. Results in this paper can offer a reference for deep learning on other more complicated NOMA techniques. Meanwhile, with the benefit of the MIMO technique, MIMO-NOMA can enhance the throughput of the communication system [1]. To date, the most universal method for MIMO-NOMA signal detection has been successive interference cancellation (SIC) reception. In the downlink, a multiuser signal is first multiplexed bysuperposition coding with a total power constraint at the base station. At the receiver, a signal suffering from channel impairment will invoke the SIC mechanism in order to decode the expected signal depending on the order of the user equipment (UE) channel gains in a cluster. The UE signal with poorer channel gain is allocated higher transmit power and is decoded first, while the UE signal with lower power allocation is treated as interference. After the signal with higher power is detected and decoded correctly, a modulation reconstruction is executed. Then, the reconstructed signal is subtracted from the received signal. The process continues until the UE can decode its desired information. From an information-theoretic perspective, SIC is an optimal multiple-access scheme in terms of the achievable multiuser capacity region in both uplink and downlink [2]. Instead of the classic SIC method, we resort to the power of deep learning--multilayer neural networks (NNs)--to achieve MIMO-NOMA signal detection. As one branch of machine learning, deep learning has made substantial progress in recent years and has been applied to many fields. The major evolution of deep learning mainly involves deep neural networks (DNNs) in pattern classification and recognition, conventional neutral networks (CNNs) in image processing, and recurrent neural networks (RNNs) in voice recognition for natural language processing (NLP). The powerful performance of deep learning has considerably changed our daily lives and the cognition of artificial intelligence (AI). Note that although Graphic Processing Unit (GPU) resources are still somewhat expensive for users, the cost in recent years has been in steady decline according to [PERSON]'s law. New products with higher performance and lower cost continue to spring up. Tensor processing units (TPUs), a customized ASIC chip for AI, were proposed by Google in 2016. It is reported that the cost of third-generation TPUs could be lower by 80%. We firmly believe that the era of AI is coming. In the field of wireless communication, although successful commercial applications related to deep learning are relatively less abundant [3], many researchers are attempting to introduce machine learning techniques into communication systems, especially for improving existing signal processing algorithms. In channel decoding, deep learning can learn a form of decoding algorithm rather a simple classifier, and successful examples have been reported, such as decoding linear code [4] and polar code [5]. Ref. [6] improves the standard belief propagation (BP) decoder to a BP-CNN and achieves improved bit error rate (BER) performance with low complexity. Compressed sensing (CS) based on deep learning theory can outperform the state-of-the-art CS-based methods in terms of both recovery quality and computation time [7]. Modulation classification and identification based on deep learning is also a popular area of research [8]. In the realm of mobile networking, the schematic of the stacked auto-encoder (SAE) works well in the applications of feature learning, protocol classification, and anomalous protocol detection [9].