IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

Abstract

Automatic modulation recognition with deep learning (DL) is challenging in distinguishing high-order modulation modes and balancing complexity against recognition accuracy. In this paper, we propose a novel dual-path modulation recognition framework named IRLNet, which consists of the improved residual stacks (IRS) and long short-term memory (LSTM). The IRS maintains the more initial residual information, learns the signal features in deep and shallow, and achieves various degrees of feature extraction. The model learns from the time domain I/Q, amplitude and phase information presented in the training data. The simulation results on RadioML2016.10B show that IRLNet performs stable in the training stage and has low spatial-temporal complexity. It also achieves a recognition accuracy of more than 93% at high signal-to-noise ratios (SNRs). Transfer learning is introduced to improve the efficiency of retraining, and robustness is proved by transferring the model to RadioML 2018.01A and HisarMod 2019.1. The simulation result shows that the training time is greatly shortened by about 25.6% in RadioML 2018.01A by introducing transfer learning. Moreover, IRLNet improves the confusion of high-order modulations and achieves a recognition accuracy of more than 90% in high SNRs.

Keywords

• Automatic modulation recognition
• deep learning
• feature extraction
• improved residual stacks
• LSTM