Year: 2025
Type: article
Abstract: 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.
Source: Electronics
Authors: Ping Tan, Dongxu Chen, Kaijun Zhou, Yi Shen, Shen Zhao
Institution: Hunan University of Technology and Business
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G5881276381, 精细手部动作想象脑电信号的识别方法研究
OpenAlex ID: W4416188171