# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 1 Introduction
It is shown that deep learning techniques and self-encoders can transmit signals more efficiently at high signal-to-noise ratios and exceed the performance of conventional spatial diversity MIMO systems. Although the above studies are significant for performance optimization of wireless communication systems, they are still at the level of local optimization of a single module. Some scholars have also started to use neural networks to optimize multiple modules in communication systems. Deep neural networks are proposed in the literature [19] to replace two signal processing modules, equalization and decoding, as a way to improve the processing of multipath signals. However, there is still a performance gap compared to the minimum MSE method with known channel statistical states. In the literature [12], a deep learning-based channel estimation and equalization approach is used to address the problems of conventional channel estimation algorithms in the presence of interference. The results show that deep learning algorithms can effectively solve the dilemma of traditional algorithms. The above studies mainly use deep neural networks to optimize one or several signal processing modules, which are simply a combination of local optimization and do not achieve complete overall optimization. To achieve overall optimization at the receiver side, the literature [20] introduces the concept of a deep receiver model that achieves end-to-end recovery of information under non-ideal channel conditions and verifies that this receiver model outperforms the conventional hard-decision receiver in terms of BER. The authors use the classical DenseNet to construct the receiver model. However, the structural design of DenseNet makes the training very difficult and cannot be adapted to the mine communication scenario. Inspired by the above work, this research aims to explore the integration of deep learning with MIMO-OFDM communication systems and design an intelligent receiver model based on convolutional neural networks. The CNN is used to substitute the whole information recovery process at the receiver side to achieve the overall optimization at the receiver side, which is used to further improve the information recovery accuracy at the receiver side of the MIMO-OFDM communication system in the complex mine environment. The main work of this research has the following points. * A new intelligent receiver model is designed for information recovery of MIMO-OFDM wireless communication systems under complex mines. The model uses convolutional neural networks to replace the information recovery processes such as channel estimation, equalization, symbol synchronization, demodulation, and decoding in conventional receivers to realize end-to-end recovery of the received signal and global optimization at the receiver side. We used the IQ signal received by the receive antenna of the MIMO-OFDM system as the input to the network model and then recovered the original bits using a multi-label classifier after feature extraction by the convolutional neural network. * We designed a VOVNet-based intelligent receiver model and conducted simulation experiments by setting a fixed modulation coding scheme in a constant channel scenario and a dynamic channel scenario, respectively. The simulation data were generated in MATLAB 2019b simulation software, and the data were fed into the model for training after a simple normalization process. The trained network model was used to predict the newly received IQ data and complete the decoding of the brand new data. * Due to the complex and changeable communication scenarios in mines, it may lead to sudden communication interruptions, resulting in the loss of communication data. Therefore, we specially performed data loss tests on the intelligent receiver model designed in this paper. The rest of this paper is organized as follows. Section 2 introduces the knowledge of the mine fading channel model and MIMO-OFDM wireless communication system.