An Automatic Modulation Classification Approach Based on an Efficient and Explainable Convolutional Neural Networks for Wireless Communication

Automatic Modulation Classification (AMC) is an essential element of cognitive radio network that provides an awareness of the spectrum as well as adaptive communication. Nevertheless, the current deep learning-based AMC schemes frequently struggle to achieve a trade-off between classification precision, computational speed, and model explainability, which are essential to real-time execution on edge computing devices. To solve these problems, this paper presents a wavelet-augmented, pruned, and quantized convolutional neural network (CNN) framework that uses Grad-CAM explainability. Evaluations on the RadioML2016.10a dataset across SNR levels from −20 dB to +20 dB are done. The suggested model obtains 99.1% accuracy at +10 dB. Although maintaining 85.6% accuracy at −4 dB SNR, SNR shows robustness under noisy surroundings. With an average inference latency of 5.2 ms and a power consumption of 5.2 W, deployment profiling on an NVIDIA Jetson Nano with a USRP B210 confirms suitability for real-time cognitive radio applications. Transparency and trust are increased when Grad-CAM visualizations verify that the model takes into account modulation-specific characteristics like envelope variations in AM signals, amplitude clusters in 16-QAM, and phase transitions in QPSK. Compared to more recent AMC approaches (2016-2024), the proposed framework is identified as the first to be both highly accurate, lightweight edge deployable, and explainable, providing a solid base to practical AMC of next-generation wireless systems.