Deep Residual Systolic Network (DRSN) for Massive MIMO Channel Estimation by Joint Training Strategies of Mixed-SNR and Mixed–Scenarios

The fifth-generation communication (5G) requires a highly accurate estimation of the Channel State Information (CSI) to take advantage of the massive Multiple-Input Multiple-Output (MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of Deep Residual Shrinkage Network (DRSN) neural network-based method that is used to solve this problem. Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then, the DRSN is used to train the noise reduction process based on the results of the LS channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed-SNR (Signal to Noise Ratio) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the Spatial-Frequency-temporal Convolutional Neural Networks (SF-CNN) with similar computational complexity and achieves better advantages in the full SNR range than the Minimum Mean Squared Error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.