Semi-blind Channel Estimation Leveraging Frequency Correlation

In massive Multiple-Input Multiple-Output (MIMO) -Orthogonal Frequency Division Multiplexing (OFDM) systems, channel estimation incurs high pilot overhead due to the high channel dimension, prompting the exploration of various algorithms to mitigate this cost. Semi-blind estimation, which usually employs a traditional iterative algorithm based on Bayesian inference, proves effective in enhancing estimation performance with a limited number of pilots. Meanwhile, Neural Network (NN)-based channel mapping and prediction methods have demonstrated potential in predicting the full channel matrix with the estimation of a proportion, reducing the pilot overhead. However, how to merge these two methods for less pilot overhead is yet to be investigated. This paper introduces a hybrid model and data driven method that combines semi-blind estimation with NN-based frequency domain channel mapping, leveraging data priors as the inference algorithm while harnessing the nonlinear mapping capability offered by NNs. The proposed architecture holds promise for extension to other applications where the synergistic combination of inference algorithms and NNs proves advantageous. Numerical results validate the effectiveness of the proposed algorithm.