###### Abstract  
Orthogonal time-frequency space (OTFS) modulation has emerged as a promising technology to alleviate the effects of the Doppler shifts in high-mobility environments. As a prerequisite to demodulation and signal processing, automatic modulation classification (AMC) is essential for OTFS systems. However, a very limited number of works have focused on this issue. In this paper, we propose a novel AMC approach for OTFS systems. We build a dual-stream convolutional neural network (CNN) model to simultaneously capture multi-domain signal features, which substantially enhances recognition accuracy. Moreover, we propose a differentiated embedded pilot structure that incorporates information about distinct modulation schemes to further improve the separability of modulation types. The results of the extensive experiments carried out show that the proposed approach can achieve high classification accuracy even under low signal-to-noise ratio (SNR) conditions and outperform the state-of-the-art baselines. automatic modulation classification; orthogonal time-frequency space; multi-domain fusion; embedded pilot 20252025254393  
20252  
processing [14]. Traditional AMC approaches primarily fall into two categories: likelihood-based AMC approaches [15; 16] and feature-based AMC approaches [17; 18]. While achieving theoretical optimality, maximum likelihood-based AMC can suffer from prohibitive computational complexity in model parameter estimation [19]. In contrast, feature-based AMC recognizes modulation schemes by leveraging characteristics such as high-order spectrum features, frequency spectrum signatures, and power spectrum attributes. In recent years, deep learning has achieved breakthroughs in different areas, e.g., image classification, speech recognition, etc. Driven by these developments, increasing research efforts have focused on integrating deep learning with modulation recognition [20]. Some works leverage convolutional neural networks to extract 2D signal features [21; 22], some works design long short-term memory (LSTM)-based networks to capture the temporal features of signals [23; 24], and a large portion of works build hybrid CNN-LSTM models that are simultaneously based on features from multiple dimensions [25; 26; 27; 28]. There are other works that develop ResNet-based hybrid neural network models [29; 30] and multi-stream/multi-scale fusion models [28; 31]. Despite these existing AMC approaches, there are very few studies focusing on AMC for OTFS systems. Additionally, it is infeasible to directly apply the existing AMC approaches, which are primarily designed for OFDM systems, to OTFS systems and achieve satisfactory classification performance [32]. An AMC approach for OTFS systems is proposed in [33] by designing a hybrid CNN and LSTM network with a residual stack to process I/Q symbols. However, this method primarily leverages the time-domain features of the received signal and does not consider the intrinsic DD domain of OTFS signals. In this paper, we investigate the AMC issue for OTFS systems by simultaneously taking into account features of the signals in both the time domain and delay-Doppler domain. To further boost the classification accuracy, we leverage embedded pilot patterns in the delay-Doppler plane to enhance the feature discriminability among modulation schemes. Embedded pilot structures have predominantly been adopted in channel estimation in OTFS systems [34; 35]. However, to date, there are no works that harness it well for modulation recognition/classification.In this work, we develop and incorporate a differentiated pilot insertion scheme in AMC that leverages the inherent properties of OTFS pilot structures and introduces modulation-specific patterns to enhance modulation discriminability. In a nutshell, our contributions are summarized as follows:  
* We propose a multi-domain fusion-based AMC approach for OTFS systems by designing a dual-stream CNN architecture that simultaneously incorporates the time-domain and DD-domain features of OTFS signals.