TFMMN: A Novel Approach for Automatic Modulation Recognition in Communication Systems

Automatic modulation recognition (AMR) plays a significant role in communication systems. Traditional AMR algorithms predominantly rely on either time-domain or frequency-domain signal information. However, relying solely on a single-domain analysis fails to capture the full range of the signal's time-varying and spectral characteristics, leading to inadequate representation of their multi-dimensional features. In this paper, we propose a novel modulation recognition architecture named Time-Frequency Multi-Modal Neural Network (TFMMN), which stands for Time-Frequency Multi-Modal Fusion. This architecture integrates traditional Convolutional Neural Network (CNN) within a Multi-channel Feature Extraction Module (MFEM) and incorporates Residual Multi-Head Self-Attention Mechanism (SA) to process signals across multiple modalities. By preprocessing the I/Q signals, we obtain amplitude and phase (A/P) signals with distinct characteristics and Fast Fourier Transform (FFT) signals. Under the feature signals of these three modalities, a multi-branch structure is constructed, and a multi-channel structure is utilized for complementary feature enhancement. We conducted experiments on the public dataset RadioML2016.10A, and the results show that our algorithm outperforms existing recognition algorithms in terms of classification accuracy. Specifically, for the challenging classification between 16QAM and 64QAM, the average classification accuracy of both modulation types exceeds 90% at a signal-to-noise ratio (SNR) of 0 dB.