  In this paper, we propose a frequency-time division network (FreqTimeNet) to
improve the performance of deep learning (DL) based OFDM channel estimation.
This FreqTimeNet is designed based on the orthogonality between the frequency
domain and the time domain. In FreqTimeNet, the input is processed by parallel
frequency blocks and parallel time blocks sequentially. By introducing the
attention mechanism using the SNR information, an attention based FreqTimeNet
(AttenFreqTimeNet) is proposed. Using 3rd Generation Partnership Project (3GPP)
channel models, the mean square error (MSE) performance of FreqTimeNet and
AttenFreqTimeNet under different scenarios is evaluated. A method for
constructing mixed training data is proposed, which could address the
generalization problem in DL. It is observed that AttenFreqTimeNet outperforms
FreqTimeNet, and FreqTimeNet outperforms other DL networks with reasonable
complexity.
