## 1 Introduction  
Automatic modulation classification (AMC) is a critical technique in wireless communication systems, serving as a key enabler for dynamic spectrum access and spectrum monitoring [1]. By automatically identifying the modulation schemes of received signals, AMC enables adaptive communication strategies, interference mitigation, and dynamic spectrum management [2, 3]. Its importance is further magnified in modern wireless networks, where diverse standards and services coexist, necessitating robust classification under complex channel conditions [1, 4]. Emerging technologies such as cognitive radio and dynamic spectrum sharing increasingly rely on AMC for efficient and intelligent spectrum utilization. In particular, adaptive modulation strategies require the receiver to identify modulation formats without prior transmitter coordination, highlighting the need for blind and reliable classification methods capable of operating under uncertainty [5, 6]. Such requirements demand classification systems that not only generalize well across modulation types and channel conditions but also operate with low computational cost, especially for real-time or resource-constrained scenarios. As communication environments grow more dynamic and heterogeneous, the development of computationally efficient and reliable AMC algorithms remains a pressing research focus. Conventional AMC methods can generally be categorized into likelihood-based and feature-based approaches [3; 7]. The likelihood-based methods utilize probabilistic models and hypothesis testing to classify modulation schemes accurately under precise channel conditions but often incur prohibitively high computational complexity due to extensive parameter estimation processes, limiting practical deployment [8; 9; 10]. Conversely, feature-based methods involve classical machine learning (ML) algorithms such as support vector machines (SVMs), decision trees, and the k-nearest neighbors (k-NN) algorithm, which classify handcrafted signal features [3; 11]. Although feature-based methods are generally simpler and computationally efficient, they often suffer from limitations related to the discriminative power of manually designed features and restricted learning capabilities inherent in conventional classifiers [12; 13]. Moreover, such handcrafted approaches may not generalize well across different channel environments or unforeseen modulation formats. Recently, neural networks (NNs) have gained considerable attention in AMC research due to their ability to automatically learn hierarchical features directly from raw input signals. By leveraging architectures such as convolutional neural networks (CNNs) [4; 14; 15], recurrent neural networks (RNNs) [16], and Transformers [17; 18], deep learning (DL)-based approaches have demonstrated strong performance across a wide range of channel conditions and signal-to-noise ratio (SNR) levels [4; 19]. These models offer greater flexibility and accuracy compared to conventional methods by capturing complex signal patterns without the need for manual feature engineering. In particular, their ability to learn from diverse signal variations makes them suitable for handling the increasing diversity of modulation schemes and channel impairments in wireless systems. Despite these advantages, DL-based AMC models typically require large amounts of data and computational resources and often utilize high-dimensional input representations that limit their practical applicability in real-time or resource-constrained environments. Moreover, many DL frameworks do not explicitly exploit the structured properties of communication signals, potentially overlooking meaningful low-dimensional features. To overcome these limitations, we propose a novel AMC framework that transforms the received signal into a Hankelized matrix and extracts its singular values to construct a compact and information-rich feature representation.