# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 1 Introduction
Reference [19] proposes a DNN-based algorithm that combines reconfigurable intelligent surfaces (RISs) to enhance predictability and security performance. Reference [20] applies the DNN network to power allocation algorithms, which can improve the performance of the downlink visible light communication NOMA system in factory automation scenarios. Reference [21] proposed an algorithm applying simultaneously transmitting and reflecting RIS (STAR-RISs) in MIMO-NOMA scenarios. The algorithm jointly optimizes the reflection, transmission phase shifts, and precoding matrix of the STAR-RIS based on each user's Quality of Service (QoS). Additionally, a convolutional neural network (CNN) model is utilized to predict the downlink spectral efficiency by analyzing user location information and channel gains. One research direction in this field involves applying deep learning methods to the receivers or detectors for MIMO-NOMA systems. Reference [22] introduces a machine-learning-based MIMO signal detector algorithm that can learn joint detection without requiring channel state information (CSI). Reference [23] proposed a DNN-based joint detector for the orthogonal frequency-division multiplexing (OFDM)-based NOMA scheme, performing channel estimation, equalization, and demodulation simultaneously. Reference [24] applies a CNN network to the scene of a single BS and multi-user downlink NOMA receivers. The convolutional layer enhances feature extraction, reducing errors introduced during the SIC detection process. Reference [25] suggests an LSTM deep learning network employing nonlinear mapping algorithms to assess data detection capacity in NOMA systems, ensuring high accuracy with low complexity. Reference [26] uses DNNto construct the precoder and SIC decoders. This approach enables the precise decoding of received signals at users through the SIC decoding technique after precoding at the transmitter. Reference [27] applies two deep learning models to the MIMO-NOMA visible light communication (MIMO-NOMA-VLC) scenario. Simulation results demonstrate that the models can achieve low BER in both perfect and imperfect SIC scenarios. Reference [28] utilizes a deep learning model with a discrete wavelet transform (DWT)-based OFDM approach. By analyzing CSI and channel noise information, this algorithm can reduce inter-channel interference. In this article, a DNN-based SIC receiver algorithm for the MIMO-NOMA scene is proposed. We apply the DNN method to traditional SIC receivers, which can reduce the errors caused by the near-far effect and enhance nonlinear detection capability. We discuss the selection of the DNN network structure and the appropriate number of hidden layers. Simulation results demonstrate the impact of the signal-to-noise ratio (SNR) on BER. Performance comparisons with traditional SIC receivers affirm the effectiveness of the DNN-based receiver algorithm. In the rest of the paper, Section 2 introduces the system model. Section 3 shows the proposed method. Section 4 presents the extensive experiments. Finally, Section 5 will summarize the conclusions.