ALWNN: Automatic Modulation Classification via Adaptive Lightweight Wavelet Neural Network

Automatic Modulation Classification (AMC) plays a crucial role in non-cooperative communication systems and is an essential component of blind signal processing. The application of deep learning methods in modulation classification has shown tremendous potential, surpassing the performance of traditional methods by a large margin. However, the high storage and computational requirements of existing deep learning methods limit their practical applications. In this paper, we propose an AMC technique using an Adaptive Lightweight Wavelet Neural Network (ALWNN) that features a streamlined design and lower computational demands. This innovative model introduces an adaptive wavelet-based feature extraction method that effectively captures information at different frequencies in the input data, ensuring classification accuracy. Additionally, the model incorpo-rates depthwise separable convolution techniques, transforming traditional convolutions into depthwise convolutions and point-wise convolutions, Substantially diminishing the count of the model’s parameters and the complexity of its computations. The proposed ALWNN model strikes a balance between efficiency and accuracy. Simulation results demonstrate that with only 9899 and 9700 parameters, it achieves accuracies of 62.14% and 63.93% on the datasets known as RML2016.10a and RML2016.10b, respectively. Furthermore, we evaluate the model in terms of Floating Point Operations Per Second (FLOPS) and Normalized Multiply-Accumulate Complexity (NMACC) to provide a more comprehensive measure of computational complexity. Compared to existing methods, ALWNN reduces FLOPS by 1.25 to 1.91 orders of magnitude and NMACC by 0.81 to 1.6 orders of magnitude.