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Transfer learning-based channel estimation in orthogonal frequency division multiplexing systems using data-nulling

Chaojin Qing1, Lei Dong1, Li Wang1

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China.

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Summary
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This study introduces a novel deep learning approach combined with transfer learning for channel estimation in OFDM systems. The method significantly improves accuracy and reduces model mismatch, outperforming existing techniques.

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Machine Learning

Background:

  • Superimposed training (ST)-based channel estimation (CE) in orthogonal frequency division multiplexing (OFDM) systems suffers from superimposed interference.
  • Data-nulling superimposed pilot (DNSP) offers a solution but faces challenges in estimation accuracy and computational complexity.
  • Deep learning (DL) shows promise for physical layer wireless communication but is susceptible to performance degradation due to model mismatch in changing wireless scenarios, necessitating network retraining.

Purpose of the Study:

  • To fuse DNSP and DL techniques to enhance channel estimation accuracy and reduce computational complexity in OFDM systems.
  • To address the model mismatch issue in DL-based CE by proposing a lightweight transfer learning (TL) network.
  • To develop a robust and accurate TL-based CE scheme for DNSP in OFDM systems that mitigates performance degradation caused by varying wireless environments.

Main Methods:

  • A hybrid approach combining DNSP with DL for channel estimation in OFDM systems.
  • Utilizing least squares estimation on a linear receiver to extract initial CE features.
  • Developing a convolutional neural network (CNN) to integrate DL-based CE solutions with DNSP.
  • Constructing a lightweight transfer learning (TL) network to overcome DL model mismatch issues.

Main Results:

  • The proposed TL-based CE scheme achieves lower normalized mean squared error (NMSE) compared to existing DNSP schemes with minimum mean square error (MMSE)-based CE across all signal-to-noise ratio (SNR) regions.
  • At 0 dB SNR, the proposed method demonstrates NMSE comparable to MMSE-based CE at 20 dB, indicating a significant improvement in estimation accuracy.
  • The scheme exhibits robustness against parameter variations, outperforming existing methods.

Conclusions:

  • The novel CE network for the DNSP scheme in OFDM systems effectively improves estimation accuracy and alleviates model mismatch.
  • The integration of DL and TL provides a powerful solution for accurate and robust channel estimation in dynamic wireless environments.
  • The proposed method offers a significant advancement over existing DNSP techniques, particularly in low SNR conditions.