Transfer Learning Guided Noise Reduction for Automatic Modulation Classification

TL;DR

This paper introduces a transfer learning guided noise reduction framework for automatic modulation classification in 6G networks, significantly improving accuracy under low SNR conditions.

Contribution

It proposes a novel deep learning based noise reduction network combined with transfer learning to enhance AMC accuracy in low SNR environments.

Findings

Achieves over 20% accuracy improvement in low SNR scenarios

Effective noise reduction in dynamic wireless channels

Enhanced classification accuracy under unstable SNRs

Abstract

Automatic modulation classification (AMC) has emerged as a key technique in cognitive radio networks in sixth-generation (6G) communications. AMC enables effective data transmission without requiring prior knowledge of modulation schemes. However, the low classification accuracy under the condition of low signal-to-noise ratio (SNR) limits the implementation of AMC techniques under the rapidly changing physical channels in 6G and beyond. This paper investigates the AMC technique for the signals with dynamic and varying SNRs, and a deep learning based noise reduction network is proposed to reduce the noise introduced by the wireless channel and the receiving equipment. In particular, a transfer learning guided learning framework (TNR-AMC) is proposed to utilize the scarce annotated modulation signals and improve the classification accuracy for low SNR modulation signals. The numerical results show that the proposed noise reduction network achieves an accuracy improvement of over 20\% in low SNR scenarios, and the TNR-AMC framework can improve the classification accuracy under unstable SNRs.