# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 1 Introduction  
The fifth generation (5G) of wireless communication technology has been widely applied to various types of services, including mobile Internet and Internet of Things (IoT) devices [1]. 5G technology has notably enhanced the transmission rates, minimized latency, and bolstered system capacity, enabling varied applications [2]. However, with the explosive growth in the number of mobile network users, the data traffic has increased 1000-fold by 2020 [3], and spectrum and time slot resources are limited [4]. The conventional orthogonal multiple access (OMA) systems face resource shortages with growing user access. To achieve the requirements for processing explosive data traffic, NOMA technology has been proposed. In a NOMA system, multiple users share the same frequency and time with distinct power allocations, thereby increasing user connections and system capacity. The earliest NOMA technology was proposed by NTT Corporation in Japan [5]. It can improve spectrum efficiency and increase system users' capacity. Recent studies have indicated that NOMA technology outperforms OMA in BER with the same bandwidth [6]. Another technology known as multiple-input multiple-output (MIMO) enables receivers with multiple antennas to communicate with multiple transmitters concurrently. In a massive MIMO scenario, terminals are equipped with numerous antennas, operating in a time division duplex, which improves throughput and radiated energy efficiency by concentrating energy into smaller spatial regions [7]. The emitted wavefronts add to the expected position and reduce their strength elsewhere. As a result, spatial multiplexing in massive MIMO significantly boosts the capacity of BS by several orders of magnitude [8]. In the MIMO-NOMA scenario, many scholars have conducted extensive research. In [9], a cooperative relay NOMA with a massive MIMO system based on the geometric-based stochastic channel model is proposed. In [10], hybrid precoding of millimeter wave large-scale MIMO based on NOMA is presented, which utilizes NOMA with beamforming to reduce the orthogonality error. Reference [11] incorporates code reuse in the proposed signal transmission scheme to access the BER of a MIMO-NOMA system. Reference [12] extends the millimeter wave mMIMO-NOMA algorithm to multiple user scenarios, and an SSOR-CRZF linear precoder is proposed. Reference [13] proposes a downlink NOMA algorithm for the 6G IoT scenario, ensuring users at different distances receive the same frequency signals from the base station. Reference [14] proposed an algorithm for distributing high-speed cached content using unmanned aerial vehicles (UAVs). Ground users can utilize these UAVs as airborne base stations, thereby minimizing the overall time latency of data transmission. While NOMA technology is efficient in wireless communication, it faces certain drawbacks [15]. The computational complexity of NOMA system receivers poses high demands on receiver hardware and software performance. Additionally, the near-far effect between users and BS leads to higher BER in traditional receivers. Effectively designing a receiver system for NOMA has become a significant challenge. To address this, we integrate deep learning algorithms into the traditional receiver model. These algorithms leverage hidden layers in neural networks to train datasets, providing convergent solutions and proving efficiency in nonlinear detection scenarios. Much research has explored the combination of NOMA and deep learning algorithms. Reference [16] provides an overview of multiple NOMA systems supporting deep learning and proposes performance evaluation metrics. Reference [17] has explored user grouping for NOMA systems and applies the deep learning algorithm to power allocation. Reference [18] utilizes deep neural networks to enhance performance in power optimization for a downlink NOMA system, leveraging the long short-term memory (LSTM) network to predict channel coefficients.