# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
###### Abstract  
Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier (HCTC) for the classification of unknown signals. The proposed method utilizes a three-stage framework to extract features from in-phase/quadrature (I/Q) signals. In the first stage, spatial features are extracted using a convolutional layer. In the second stage, temporal features are extracted using a transformer encoder. In the final stage, the features are mapped using a deep-learning network. The proposed HCTC method is investigated using the benchmark RadioML database and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method achieves a better performance in modulation signal classification. Additionally, the performance of the proposed method is evaluated when applied to different batch sizes and model configurations. Finally, open issues in modulation recognition research are addressed, and future research perspectives are discussed. m _et electronics_ Article  
HCTC: Hybrid Convolutional Transformer  
Automatic Modulation Recognition (AMR) involves automatically categorizing the modulation type from received complex-valued raw radio signals without any prior understanding of the signal information or channel properties. In modern communications and signal-processing systems, AMR guarantees reliable communication in diverse wireless environments, increases spectral efficiency, and improves signal detection [1]. However, the radio environment is becoming more chaotic because of recent advancements in wireless communications technologies, making AMR more challenging [2]. Conventional AMR techniques can be divided into two basic categories: feature based (FB) and probabilistic likelihood based (LB). LB methods use the likelihood function for each signal and model multiple hypotheses based on the number of signal types. The performance of the classifier is obtained using the likelihood ratio test [3]. In contrast, FB methods involve the extraction of meaningful features from the received signal that are then used for classification [4]. Typical LB methods include the average likelihood ratio test (ALRT) [5, 6], generalized likelihood ratio test (GLRT) [7], and hybrid likelihood ratio test (HLRT) [8]. Common types of features used in FB methods include higher-order statistical features [9], cyclic features [10], constellation shapes [11], entropy features [12, 13], and transform domain features [14]. The FB methods classify the modulation signal by the further use of classifiers, such as an artificial neural network (ANN) [9, 15], a fuzzy-logic-based classifier [16], a support vector machine (SVM) [17, 18], a hidden Markov model (HMM) [19], a polynomial classifier [20], and K-nearest neighbors (KNNs) [21]. The LB method possesses high computational complexity and requires knowledge of thecommunication channel's information [22]. However, the FB method relies on a known threshold of the extracted features and the classifier, which is not the best suited for the selected features, making it insufficiently discriminative over complex modulation types [23]. For efficient feature extraction and radio signal classification, deep-learning (DL) and machine-learning (ML) techniques address the limitations of the FB and LB methods. A convolutional-neural-network (CNN)-based model was developed that utilized in-phase/quadrature (I/Q) data for AMR [24]. A long short-term memory (LSTM) model was proposed for AMR, considering the variable symbol rate, which outperformed the CNN-based model [25]. A combination of LSTM and a denoising autoencoder framework was proposed, with the framework found to be capable for extracting robust features from noisy signals [26].