A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks
This addresses channel estimation in wireless communications, specifically for 5G systems, but appears incremental as it applies existing CNN methods to a standard dataset.
The paper tackled MIMO-OFDM channel estimation by designing 2D and 3D CNN architectures to interpolate channel values from reference signals, achieving performance tested on a 5G NR dataset and integrated with MIMO detection to evaluate bit error rate.
In this paper, we study the usage of Convolutional Neural Network (CNN) estimators for the task of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, the CNN estimators interpolate the channel values of reference signals for estimating the channel of the full OFDM resource element (RE) matrix. We have designed a 2D CNN architecture based on U-net, and a 3D CNN architecture for handling spatial correlation. We investigate the performance of various CNN architectures fora diverse data set generated according to the 5G NR standard and in particular, we investigate the influence of spatial correlation, Doppler, and reference signal resource allocation. The CE CNN estimators are then integrated with MIMO detection algorithms for testing their influence on the system level Bit Error Rate(BER) performance.