# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 1 Introduction
A typical representative is ComNet [7] proposed by the team from Southeast University. Subsequently, SwitchNet [8], also based on a model-driven approach, was proposed to address performance issues in ComNet. Additionally, DCCN [8], based on a complex convolutional structure, includes a receiver, but its code is not open-source, making it difficult to reproduce the simulation links and training data. On the other hand, some individual achievements in researching deep learning MIMO receivers have been published in recent years. These studies focus on the design of single-carrier MIMO receivers. In 2018, [PERSON] et al. proposed OAMP-Net [9], which uses a multi-layer iterative neural network approach to design MIMO receivers. In current practical applications, MIMO and OFDM are often combined, known as MIMO-OFDM. Compared to pure MIMO or OFDM techniques, the signal transmission process of MIMO-OFDM is more complex. Factors such as the number of antennas and the number of subcarriers and the specific characteristics of OFDM, such as cyclic prefix and peak clipping, increase the complexity of the communication process. Therefore, this paper aims to expand the aforementioned research by designing a small-scale MIMO-OFDM receiver that incorporates the principles of deep learning. The link of the DL-based MIMO-OFDM receiver is shown in Figure 1. Specifically, in this research, we start by studying the reception scheme for OFDM signals and extending it to MIMO-OFDM receiver design. Deep learning methods are employed to conduct research in this field, treating the entire receiver as a black box and utilizing deep neural networks to perform tasks such as channel estimation and signal detection. Furthermore, a deep-learning-based receiver called DMCNet is proposed [10], which combines complex convolution and complex, fully connected structures. This receiver network aims to recover the transmitted signals from the received signals and validate their accuracy and robustness under different channel conditions. Finally, based on the data-driven model DMCNet, a model-driven approach is proposed. Experimental results demonstrate that the model-driven approach, when combined with relevant algorithms from traditional communications, achieves a higher accuracy and requires fewer parameters in certain scenarios. Motivated by the discussion above, we design a model-driven multi-pilot convolution neural network for MIMO-OFDM receivers (DMCNet-pro). Comparing to previous work, a signal denoising module is added to denoise the pilot frequency and data, respectively, in DMCNet-pro. The network we propose shows great performance regarding accuracy and robustness. Additionally, fewer parameters are required for this network in some scenarios. The rest of this paper is organized as follows. Section 2 introduces the dataset and the channel model. Section 3 introduces the structure of DMCNet. Section 4 introduces the structure of DMCNet-pro. Section 5 introduces the experimental design and results. Section 6 introduces the conclusion of the experiment. We only consider a simplified single user scenario for a clear mathematical analysis, establishing fundamental principles before tackling complexities like multi-user interference in realistic settings.