# MIMO Signal Detection Based on IM-LSTMNet Model
## 1 Introduction
Addressing the limitations of the path elimination QR decomposition algorithm, [PERSON] et al. proposed a signal detection method with high energy efficiency and low complexity that is based on adaptive QR with near-optimal error performance in a MIMO-OFDM system [8]. In recent years, with artificial intelligence (AI) rapidly developing, intelligent signal processing has attracted more and more attention. Deep learning (DL), a subset of AI, enhanced computers' intelligence. DL has proven effectiveness in achieving this goal. A host of deep learning methods have been applied to signal detection [9; 10; 11]. Long short-term memory (LSTM) networks, a type of deep learning network, were proposed in 1997 to solve the long-term dependence problem in recurrent neural networks (RNN) [12]. Additionally, LSTM networks can catch nonlinear features in time series data more broadly. Because of the influence of pilot and cyclic prefix (CP) length, it can effectively deal with the problems of gradient disappearance, gradient explosion, and low  
Figure 1: MIMO system structure diagram. estimation accuracy of traditional orthogonal frequency division multiplexing (OFDM) systems. Additionally, an LSTM network can easily handle the problem of long sequence dependence. An LSTM network can be applied to a MIMO-OFDM system for signal detection. Convolutional neural networks (CNNs) are some of the most representative and important network structures in deep learning. Compared with other deep learning methods, CNNs can extract features and gain important information better. Therefore, scholars have combined CNNs and LSTM to extract spatio-temporal features from input data more efficiently. Attention mechanisms can be utilized to emphasize important points across diverse information sources. The combination of attention mechanism and convolutional neural network-long short-term memory (CNN-LSTM) has been proven to be applicable to OFDM system for enhancing signal detection performance. To enhance the signal detection capability of OFDM systems, [PERSON] et al. proposed a signal detection method of an OFDM system based on an improved LSTM neural network [13]. They took an improved Chameleon Swarm algorithm to enhance LSTM's initial hyperparameters, trained the CLCSA-LSTM model, and used the trained CLCSA-LSTM network model to detect signals in the OFDM system. [PERSON] proposed a receiver that uses LSTM to take low spectrum in OFDM wireless systems over Rayleigh fading channels [14]. Prior to online deployment and data retrieval, the proposed LSTM estimator uses offline training to collect channel state information from the sending and receiving part. [PERSON] has proposed a new architecture, StressNet, which is a combination of two-dimensional CNN and LSTM networks, and applies them to detect stress states in electrical EEG signals [15]. [PERSON] proposes a deep convolutional long short-term memory (ConvNet-LSTM) auxiliary receiver for detecting signals in downlink NOMA systems [16]. The proposed technique implicitly estimates the channel state information and directly recovers the transmitted symbol. [PERSON] proposed an underwater acoustic (UWA) OFDM receiver based on deep learning and expertise [17]. The receiver combines deep learning with classic expertise in block-based signal processing in UWA OFDM to enhance both system performance and interpretability. It designs a skip connection CNN and cascaded attention mechanism-enhanced Bidirectional LSTM (BiLSTM) networks for joint channel estimation and signal detection. Signal detection is very significant in MIMO-OFDM systems, but some classical detection methods do not deal well with nonlinear problems in wireless channels, such as the nonlinear distortion of wireless channels. The network layers in the neural network model, such as ReLU layer and sigmoid layer, can better handle nonlinear data. The neural network can deal with nonlinear distortion better and improve the signal detection performance of the MIMO system.