# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 1 Introduction  
Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are key technologies used in current 5G and even future 6G wireless communications [1]. OFDM enables parallel transmission of signals by using overlapping orthogonal sub-channels to transmit data. The data streams are modulated onto various sub-channels. Its main advantages are the high spectrum efficiency and the ability to cope with adverse channel conditions without complex equalization filters. MIMO technology utilizes multiple antenna arrays at both the transmitting and receiving ends of the system. At the transmitting end, the serial data streams are parallelized through space-time transformations and are sent out through different transmitting antennas. At the receiving end, multiple antennas are used to receive the signals, and the received signal is processed through decoding modules. MIMO technology fully utilizes spatial and temporal diversity and multiplexing gains, significantly increasing the channel capacity and spectrum efficiency without increasing the occupied bandwidth [2]. However, the communication environment is becoming increasingly complex, and the transmission of signals is subject to various unfavorable factors, making it increasingly difficult to accurately receive signals. Traditional multi-antenna, multi-carrier signal receiver schemes often require channel estimation, symbol detection, and other steps. In multi-antenna scenarios, researchers have proposed optimal signal detection algorithms, also known as Maximum Likelihood (ML) detection algorithms [3]. These methods exhaustively search through all possible transmitted signals to find the optimal solution, providing the best performance in terms of solution accuracy, but they come with a high computational complexity. Commonly used linear signal detection algorithms include the Zero Forcing (ZF) detection algorithm [4] and the Minimum Mean Square Error (MMSE) detection algorithm [5]. The ZF algorithm is a linear precoding technique used in wireless communication. It aims to eliminate interference by designing a matrix that cancels out signals from unwanted users, improving system performance, especially in multi-user scenarios. The ZF algorithm has a simple solution but suffers from significant performance degradation when the noise power is high. The MMSE detection algorithm takes noise into account and performs better than the ZF algorithm at lower signal-to-noise ratios. However, in increasingly complex communication environments, these traditional methods exhibit poor robustness, susceptibility to interference, and limited accuracy. Therefore, in the field of wireless signal reception, there is a need for new technologies to complement and optimize traditional methods. Fortunately, with the continuous development of computer hardware, the training capability of deep neural networks has become stronger. In recent years, the development of deep learning theory has been rapid, and related research has been applied to the field of wireless communications. Compared to traditional approaches, deep learning algorithms learn knowledge automatically from existing data and experimental results, surpassing the limitations of traditional mathematical approaches and exhibiting superior performance and adaptability in certain scenarios. Reference [6] first proposed the design of a single-antenna OFDM receiver using a five-layer fully connected structure. The fully connected network does not require explicit channel estimation and signal detection, treating the entire receiver as a black box. This breaks the modular structure of traditional approaches, and the experimental results demonstrate that the neural-network-based approach exhibits stronger robustness. Model-driven approaches combine deep learning with expert knowledge in the field of wireless communications. The receiver is divided into two sub-networks: channel estimation (CE) and signal detection (SD).