EDDA:An Efficient Divide-and-Conquer Domain Adapter for Automatics Modulation Recognition

Abstract

The development of deep learning technology has injected new vitality into the task of automatic modulation recognition (AMR). Despite achieving promising progress, existing models tend to lose recognition capability in low-quality communication environments due to the neglect of latent distributions within the data, i.e., classifying samples in a single feature space, resulting in unsatisfactory performance. Motivated by this observation, this paper aims to rethink the modulation signals classification from a new perspective on the latent data distribution. To address this, we propose a novel efficient divide-and-conquer domain adapter (EDDA) for AMR tasks, significantly enhancing the existing model's performance in challenging scenarios, irrespective of its architecture. Specifically, we first follow a divide-and-conquer approach to divide the raw data into multiple sub-domain spaces by signal-to-noise ratio (SNR), and then encourage the domain adapter to estimate the latent distributions and learn domain internally-invariant feature projections. Subsequently, we introduce a dynamic strategy for updating domain labels to overcome the limitations of the initial domain label partition by SNR. Finally, we provide theoretical support for EDDA and validate its effectiveness on two widely used benchmark datasets, RadioML2016.10a and RadioML2016.10b. Experimental results show that EDDA achieves average accuracy improvements of 11.63% and 2.32% on the respective datasets. Theoretical and experimental results demonstrate the superiority and versatility of EDDA.