Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach

This paper represents a new method for the extraction of features from 5G signals using spectrogram and quantum cat swarm optimization (QCSO). The proposed approach uses a discrete wavelet transform (DWT)-based convolutional neural network (W-CNN) to enhance the extracted features and improve the signal classification. The combination of QCSO and W-CNN is designed to enable improved signal recognition and dimension reduction. Our results demonstrate an improvement in the 5G signal feature extraction performance with the use of this novel approach. The QCSO shows improvement in seven out of eight parameters studied when compared to five other state-of-the-art optimization methods.