Deep OFDM Channel Estimation: Capturing Frequency Recurrence
This addresses channel estimation for wireless communication systems, offering an incremental improvement over existing deep-learning techniques.
The paper tackles channel estimation in OFDM systems by proposing SisRafNet, a deep-learning method that uses recurrent neural networks to exploit frequency correlation, achieving superior performance validated on 3GPP-compliant channels across various signal-to-noise ratios.
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.