Optimizing Deep Learning Based Channel Estimation using Channel Response Arrangement

The techniques used in deep learning for channel estimation are generally model-centric. These models have changed significantly over the years with each iteration yielding a better estimator than the last. Fundamentally, channel estimation works by exploiting correlations in an array of complex numbers, in particular the channel gains for a fading channel. In this paper, we study the effects of the spatial arrangement of channel response and input data, on channel estimation. With the right spatial arrangement, we improved the performance of our convolutional neural network that was used for estimation. Additionally, we optimized the training procedure simultaneously. We experimentally validate the importance of spatial arrangement of data in obtaining an accurate deep learning model for the channel.