An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction

Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of -8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes.