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In this paper, we study the usage of Convolutional Neural Network (CNN)estimators for the task of Multiple-Input-Multiple-Output Orthogonal FrequencyDivision Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, theCNN estimators interpolate the channel values of reference signals forestimating the channel of the full OFDM resource element (RE) matrix. We havedesigned a 2D CNN architecture based on U-net, and a 3D CNN architecture forhandling spatial correlation. We investigate the performance of various CNNarchitectures fora diverse data set generated according to the 5G NR standardand in particular, we investigate the influence of spatial correlation,Doppler, and reference signal resource allocation. The CE CNN estimators arethen integrated with MIMO detection algorithms for testing their influence onthe system level Bit Error Rate(BER) performance.

A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks