Deep OFDM Channel Estimation: Capturing Frequency Recurrence
- URL: http://arxiv.org/abs/2401.05436v1
- Date: Sun, 7 Jan 2024 14:13:08 GMT
- Title: Deep OFDM Channel Estimation: Capturing Frequency Recurrence
- Authors: Abu Shafin Mohammad Mahdee Jameel, Akshay Malhotra, Aly El Gamal, and
Shahab Hamidi-Rad
- Abstract summary: We propose a deep-learning-based channel estimation scheme in an OFDM system.
We employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints.
The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques.
- Score: 10.76835122839777
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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