LSRN: A Recurrent Residual Learning Framework for Continuous Wireless Channel Estimation Using Super-Resolution Concept

As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem. To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN. Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements. In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks. Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error. Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.