Modulation Recognition Using Signal Enhancement and Multistage Attention Mechanism

Abstract

Robustness against noise is critical for modulation recognition (MR) approaches deployed in real-world communication systems. In MR systems, a corrupted signal is normally enhanced using low-level signal enhancement (SE) before signal classification (SC). Many existing approaches address signal distortion problems by compartmentalizing SE from SC. While those approaches allow for efficient development, they also dictate compartmentalized performance metrics, without feedback from the SC module. For example, SE modules are designed using perceptual signal quality metrics but not with SC in mind. To improve the effectiveness of SE on MR, this paper proposes a joint learning framework consisting of three cascaded modules: dual-channel spectrum fusion, SE, and SC. Instead of separately processing SE and SC, these three modules are integrated into one framework and jointly trained with a single recognition loss. In contrast to estimating clean signals, the SE module in the proposed joint learning framework is trained to predict a ratio mask and find important time-frequency bins for the SC module. We integrate a multistage attention mechanism into the framework to further increase the robustness. The multistage attention mechanism is deployed to strengthen the recognition-related features learned from context information in channel, time, and frequency domains. We evaluate the recognition performance of the proposed framework and its modules on two benchmark datasets: RadioML2016.10a and RadioML2016.10b. The experiment results show that the proposed joint learning framework outperforms the separate learning framework. Moreover, comparisons are performed with several existing learning-based MR methods in the literature. The proposed joint learning framework leads to significant performance improvement, especially for modulated signals corrupted by channel noise.