Signal Modulation Recognition Based on DRSLSTM Neural Network

To overcome the challenge of degraded classification accuracy in automatic modulation recognition under low signal-to-noise ratio (SNR) conditions, this paper introduces an end-to-end framework utilizing a Deep Residual Shrinkage Long Short-Term Memory (DRSLSTM) network. The proposed DRSLSTM architecture synergistically integrates two dedicated components: a deep residual shrinkage module, specifically designed for I/Q signals to perform simultaneous denoising and spatial feature extraction, and a Long Short-Term Memory (LSTM) network that captures long-range temporal dependencies from the refined feature sequences. Extensive simulations on a public dataset show that the DRSLSTM model achieves a recognition accuracy of 51.19% at −8 dB SNR, an improvement of 3.36 percentage points over the CLDNN baseline, and consistently surpasses six benchmark models at SNR levels above 0 dB. Moreover, it exhibits higher average recognition accuracy across a wide range of SNR conditions. The experimental results validate the overall superiority of the proposed approach.