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 signals are processed by parallel frequency blocks first and then go through parallel time blocks. Using 3rd Generation Partnership Project (3GPP) channel models, the mean square error (MSE) performance of FreqTimeNet 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 FreqTimeNet outperforms other DL networks, with acceptable complexity.

翻译：在本文中,我们提议建立一个频率-时间分区网络(FreqTimeNet),以改善基于 OFDM 频道估计的深层学习(DL) 的绩效。 FreeqTimeNet 是根据频率域与时间域之间的正方位值设计的。在 FreeqTimeNet 中,输入信号先由平行频率区块处理,然后通过平行时间区块处理。使用第三代伙伴关系项目(GPP) 频道模型,评估了FreeqTimeNet在不同情景下的平均平方差(MSE) 性能。提出了构建混合培训数据的方法,这种方法可以解决DL 中的通用问题。 观察到FreqTimeNet 与其他DL 网络相比,具有可接受的复杂性。