# MIMO Signal Detection Based on IM-LSTMNet Model
## 1 Introduction  
In the field of modern communication, multiple-input multiple-output (MIMO) technology is a significant technological innovation. MIMO technology improves the data transmission speed and reliability of wireless communication system by using multiple antennas to transmit and receive signals. The principle of MIMO technology is based on spatial diversity and channel diversity. Spatial diversity refers to the simultaneous transmission of signals through multiple antennas over different transmission paths, thus increasing the reliability of the signal [1]. Channel diversity means that the signal is received through multiple antennas in different channel environments, reducing the transmission error of the signal. The multiple antennas in the MIMO system can be divided into transmitting antennas and receiving antennas, and the system structure diagram is in Figure 1. MIMO systems can divide data into \(n\) data streams, which are sent simultaneously at the transmitter through different antennas. At the receiver, the MIMO system can receive and decode these signals through \(n\) antennas. Due to the existence of separate channels between antennas, MIMO systems can transmit multiple signal streams simultaneously, which increases the data transfer rate and system capacity. The benefits of MIMO technology include increased data transmission rates, increased system capacity, improved signal reliability, strong anti-interference capabilities, and increased coverage [2]. Massive MIMO has higher system capacity and faster transmission rates on the basis of MIMO systems, and can also be beamforming [3]. However, the Massive MIMO system is generally an antenna array, which is complex. Its antenna size and power consumption are large. Massive MIMO systems cannot operate in certainscenarios where resources are limited or hardware requirements are low, such as signal processing on Radio Frequency Identification systems. Compared with Massive MIMO, the MIMO system is simpler and less complex, which means that it can be better applied to resource-limited scenarios [4]. These make MIMO technology widely used in modern wireless communication systems and bring great impetus to the development of the wireless communication field. This paper focuses on the research of MIMO systems. Because of the complicated radio communication environment, the signal from the transmitter is influenced by noise and other factors, making it extremely difficult to recover the signal. For the accurate recovery of transmission signals and the improvement of communication quality, it is imperative to detect the signal appropriately. Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is one of the most popular technologies in 4G, 5G New Radio, and so on. MIMO techniques provide extra freedom in space and enhance channel capacity by spatial multiplexing. In order to achieve MIMO capacity gain, it is crucial to recover the plurality of transmitted data streams from multiple receiving antennas [5]. For MIMO-OFDM systems, the multi-antenna structure can improve the signal attenuation problem and increase the signal transmission rate. However, the signal received by a receiving antenna is often an alias of the signals transmitted by different transmitting antennas, and the channel gain and noise of different signals make the signal detection process of the receiver more complicated. Therefore, the signal detection problem has always been a hot research direction in MIMO systems. [PERSON] proposed a signal detection of the genetic algorithm for the massive MIMO physical layer spatial optical communication systems [6]. Additionally, a signal detection technique utilizing a genetic algorithm has been developed specifically for MIMO-OFDM systems. [PERSON] proposed an integrated MIMO-OFDM-IM system joint signal processing scheme based on sparse reconstruction [7]. An enhanced sequential Monte Carlo signal detection approach is presented to achieve efficient detection of communication signals.