A Massive MIMO Channel Estimation Method Based on Hybrid Deep Learning Model With Regularization Techniques

Abstract

The channel estimation technique is crucial for the development of wireless communication systems. By accurately estimating the channel state, transmission parameters such as power allocation, modulation schemes, and encoding strategies can be optimized to maximize system capacity and transmission rate. In this paper, we propose a hybrid deep learning model for channel estimation in multiple-input multiple-output (MIMO) wireless communication system. By combining the advantages of convolutions and gated recurrent units (GRUs), the generalization capability of deep learning models across various wireless communication scenarios can be fully utilized. Furthermore, a series of regularization techniques such as data augmentation and structural complexity constraints have been introduced to avoid overfitting problems. The stochastic gradient descent (SGD) based on error backpropagation is used to iteratively train the model to convergence. During the simulation process, we have validated the effectiveness of the hybrid deep learning model on two wireless channel conditions, including quasi-static block fading and time-varying fading condition. All the samples are generated offline with SNRs from 10 to 40 dB with a step size of 5 dB. The comparison results with a series of conventional methods and deep learning models have proven the effectiveness of the proposed method.