With the increasing demand for reliable and efficient V2X (Vehicle-to-Everything) communications in cognitive radio environments, spectrum sharing becomes imperative. In this context, accurate modulation classification serves as a fundamental component for efficient spectrum sensing and allocation. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) trained on spectrograms of BPSK and QPSK modulation schemes for automatic modulation classification in V2X scenarios. Experimental results demonstrated the effectiveness of the proposed CNN-based framework in accurately classifying modulation schemes in V2X communications.

This paper presents a novel deep learning-based automatic modulation recognition (AMR) model, designed to classify ten modulation types from complex I/Q signal data. The proposed architecture, named CV-CNN-TCN, integrates Complex-Valued Convolutional Neural Networks (CV-CNNs) with Temporal Convolutional Networks (TCNs) to jointly extract spatial and temporal features while preserving the inherent phase information of the signal. An enhanced variant, CV-CNN-TCN-DCC, incorporates dilated causal convolutions to further strengthen temporal representation. The models are trained and evaluated on the benchmark RadioML2016.10b dataset. At SNR = −10 dB, the CV-CNN-TCN achieves a classification accuracy of 37%, while the CV-CNN-TCN-DCC improves to 40%. In comparison, ResNet reaches 33%, and other models such as CLDNN (convolutional LSTM dense neural network) and SCRNN (Sequential Convolutional Recurrent Neural Network) remain below 30%. At 0 dB SNR, the CV-CNN-TCN-DCC achieves a Jaccard index of 0.58 and an MCC of 0.67, outperforming ResNet (0.55, 0.64) and CNN (0.53, 0.61). Furthermore, the CV-CNN-TCN-DCC achieves 75% accuracy at SNR = 10 dB and maintains over 90% classification accuracy for SNRs above 2 dB. These results demonstrate that the proposed architectures, particularly with dilated causal convolutional enhancements, significantly improve robustness and generalization under low-SNR conditions, outperforming state-of-the-art models in both accuracy and reliability.

The rapid advancement of connected vehicle technologies has intensified the need for efficient spectrum utilization. Cognitive radio (CR) enables dynamic access to underutilized spectrum, with spectrum sensing playing a key role in detecting primary users (PU). This study introduces a novel spectrum sensing approach that integrates filter bank (FB) signal decomposition with convolutional neural networks (CNNs)—referred to as filter bank decomposition and convolutional neural network (FB-CNN)—to enhance detection performance compared to conventional methods. Unlike traditional techniques such as energy detection (ED), the proposed FB-CNN leverages the frequency components extracted by the FB as CNN inputs, enabling robust identification of PU signals across multiple modulation schemes, including BPSK, QAM, FSK, and GMSK. Simulation results demonstrate substantial gains, particularly in low-SNR scenarios: for example, at an SNR of 5 dB, FB-CNN achieves a detection probability of 89% for 2-FSK, compared to only 22% with ED—representing a fourfold improvement. These findings highlight the novelty and effectiveness of FB-CNN in significantly improving spectrum sensing reliability for connected vehicle networks operating in challenging signal environments.