Self-Weighted Convolutional Neural Networks for RF Signal Classification in Support of Wireless Spectrum Awareness

RF signal classification provides modulation information that can support wireless spectrum awareness after signal acquisition and segmentation. This paper presents a self-weighted convolutional neural network for classifying communication-signal modulation formats from in-phase/quadrature (IQ) sequences. In the proposed multi-branch block, the normalized output channels of one convolutional branch are multiplied by learnable scaling coefficients before aggregation. Stacked blocks are followed by squeeze-and-excitation recalibration and a fully connected classification head. The network is evaluated on a MATLAB-generated 12-class IQ dataset under variations in network depth, symbol rate, carrier-frequency offset, and transfer settings, and is compared with Inception, ResNet50, CaiT, and ViT for Small-Size Datasets. An institute-provided IQ dataset with the same modulation categories provides an additional within-dataset evaluation. On the synthetic dataset, the proposed network achieves an average test accuracy of 91.91%, 0.52 percentage points above Inception. Fine-tuning on a small target-domain subset achieves 91.81% average accuracy while reducing the observed convergence requirement from 21 to 5 epochs compared with training from scratch on the target dataset. On the institute-provided dataset, the proposed, transfer, and Inception networks achieve 93.61%, 92.75%, and 92.32%, respectively. These results support the proposed method as a modulation-aware RF signal-classification component for wireless spectrum-awareness workflows under the evaluated synthetic and institute-provided data settings.