Deep OFDM Channel Estimation: Capturing Frequency Recurrence
Abu Shafin Mohammad Mahdee Jameel Akshay Malhotra Aly El Gamal Shahab Hamidi-Rad
In this paper, we propose a deep-learning-based channel estimation scheme in
an orthogonal frequency division multiplexing (OFDM) system. Our proposed
method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is
based on a novel study of recurrent models for exploiting sequential behavior
of channels across frequencies. Utilizing the fact that wireless channels have
a high degree of correlation across frequencies, we employ recurrent neural
network techniques within a single OFDM slot, thus overcoming the latency and
memory constraints typically associated with recurrence based methods. The
proposed SisRafNet delivers superior estimation performance compared to
existing deep-learning-based channel estimation techniques and the performance
has been validated on a wide range of 3rd Generation Partnership Project (3GPP)
compliant channel scenarios at multiple signal-to-noise ratios.