# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 1 Introduction  
With the advancement of the industrialization revolution, the coal mining industry is also changing regarding intelligence and intensification. As an important aspect of intelligent coal mine construction, the intelligence of a coal mine communication system has become a topic worthy of study [1; 2; 3]. As a restricted space, mine tunnels are much more complex than surface communication scenarios, and there are many branches and bends distributed in the narrow space. Therefore, there are serious transmission losses in mine communication. When electromagnetic waves propagate in mines, it will be absorbed by the denser coal dust and water vapor in the air or reflected by the uneven roadway rock walls and obstacles, thus causing serious transmission losses [4]. Due to multipath fading [5], the transmission characteristics of the mine signals are destroyed, which makes the communication system less reliable. With the innovation of communication technology, some of the communication technologies that have been used maturely in terrestrial communication have been introduced into mine communication systems. For example, MIMO-OFDM technology is considered as one of the most important transmission technologies in wireless communication systems because of its excellent transmissionperformance [6]. MIMO systems improve channel capacity and OFDM uses multi-carrier technology to resist frequency selective fading caused by non-ideal factors during transmission, which also improves the system reliability to some extent. However, MIMO-OFDM technology also has many limitations. For example, MIMO systems need to achieve a high level of accuracy in channel state information and keep the channel stable, which is not guaranteed in real communication. In addition, with the increased number of antennas, the complexity of signal processing at the receiver increases. Recently, rapidly developing artificial intelligence (AI) has brought new solutions to many fields, especially deep learning, as one of the most outstanding branches of AI, which has made important contributions in fields such as computer vision (CV) and natural language processing (NLP) [7; 8; 9]. Therefore, a growing number of researchers have started to study the application of deep learning in communication systems, hoping that deep learning techniques can be used to solve communication challenges that are difficult to be solved by traditional communication algorithms. There are many studies on the introduction of deep learning into communication systems, including channel estimation [10], channel decoding [11], channel equalization [12], channel modeling [13], modulated signal identification [14], or other local performance optimization. For signal modulation identification, the literature [15] addresses cognitive radio and proposes the use of deep learning methods for automatic signal modulation identification. The authors used a data-driven approach to train CNNs on different datasets to achieve higher accuracy signal recognition. For channel estimation, the literature [16] utilizes deep neural networks for channel estimation and signal detection in OFDM systems and demonstrates that the deep learning approach has some improvements on the system BER performance under severe wireless channel interference. For signal decoding, a deep neural network scheme for polar coded short packet decoding was used in the literature [17]. A decoding method for decoding polar codes in a flat fading channel is designed using a deep learning approach. Simulation results show that the method can obtain the coding gain under the fading channel with a simple codebook structure. For MIMO communication systems, a self-encoder-based unsupervised deep learning scheme for the physical layer of single-user MIMO communication is proposed in the literature [18]. The study combines multiple MIMO assignments into one end-to-end optimization task as a way to reduce the BER of signal transmission.