A Complex-Valued Transformer for Automatic Modulation Recognition

Abstract

Automatic modulation recognition (AMR) is a widely used technique in various communication systems. In this work, we propose a complex-valued transformer (CV-TRN) network for AMR. Considering the in-phase (I) and quadrature (Q) components of the signal are two consistent data with only a phase difference of π/2, they can teach the network independently which in disguise augment the training data, but the I/Q components are collectively needed to measure similarity in the multi-head self-attention (MHSA). We input the I/Q data individually into the network with shared parameters, and they are transmitted independently in the network except in the MHSA, where a complex-valued MHSA (CMHSA) is proposed to let the information from I/Q components integrate. Moreover, CV-TRN adopts the relative position embedding, with a mathematical analysis of its advantages for AMR. A data augmentation method of random phase offset is introduced to further improve the robustness. Experimental results on RML2016.10a and RML2018.01a datasets demonstrate that the proposed CV-TRN outperforms state-of-the-art AMR methods and is parameter-efficient.