# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 2 Related Works  
The huge demand for wireless communication network protocols may be due to the increasing complexity of spectra in dynamic band allocation, especially within unlicensed bands [10]. To overcome these issues, spectrum sensing is adopted, as it enables the allocation of bands in multidimensional states. The signal categorization or modulation mechanism helps identify spectrum-sensing signals. In [10], the deep signal identification network (DSINet) was introduced. This algorithm helps identify the signal strength based on features such as time, frequency, and power. Meanwhile, [PERSON] et al. proposed the use of CNN-based models to detect and identify 5G spectrum signals in wireless communication networks [11]. These models can perform classification without prior communication, operating on two different submodels, such as CASE1 and CASE2. These models can also enable the determination of the signal strength based on convolutional features as well as the removal of unwanted dropout features. [PERSON] et al. (2023) proposed an RNN-based deep learning model for spectrum detection based on feedback from output states [12]. The efficacy of such models can be assessed using an empirical test using ADALM-PLUTO [13, 14]. The application of wavelet transforms in signal identification has been extensively explored in various domains. A computational algorithm was developed by [PERSON] (2006), who utilized wavelet-based transformation to detect power frequency variations, subharmonics, integer harmonics, and interharmonics in power signals. The mentioned study used a continuous wavelet transform (CWT) and a discrete stationary wavelet transform (SWT) to identify and quantify amplifiers of the harmonic frequencies present in the power signal. Ref. [15] focused on employing a wavelet transform in electrocardiogram (ECG) signal analysis for identification purposes. The researchers developed a reliable method to detect R peaks and QRS complexes, using the DWT to extract vital information from the ECG signal. The precise detection of the temporal locations of the R peaks and QRS complex is crucial to the effectiveness of subsequent stages of ECG processing. The mentioned study exhibited a detection precision of up to 99%. Ref. [16] described the use of an optimal-scale wavelet transform to detect weak ultrasonic signals. They explored noise suppression methods using wavelet transforms and introduced a technique to identify the best scale by enhancing the entropy of discrete wavelet transform (DWT) coefficients. This approach was effective in addressing highly noisy ultrasonic signals and proved to be significantly more time-efficient than approaches using traditional wavelet transforms. In the field of speaker identification, ref. [8] investigated the use of a discrete wavelet packet transform (WPT) in security systems. In their study, they presented a speaker identification system based on the energy of speaker utterances, incorporating signal pre-processing, feature extraction via a wavelet packet transform, and speaker identification through an artificial neural network. The experimental results demonstrated the effectiveness of the proposed speaker identification system. Ref. [17] focused on detecting power quality disturbances using a discrete wavelet transform. The author evaluated various types of disturbances in power quality and proposed the application of a wavelet transform for the rapid and sensitive detection of such disturbances. The above review of the literature highlights the practical applications of wavelet transforms in various fields, including power quality analysis, ECG signal analysis, ultrasonic signal identification, and speaker identification, underscoring the value of wavelet transforms in signal identification and analysis. Metaheuristic optimization techniques have been extensively used in feature selection in various fields. [PERSON] et al. (2022) employed a wavelet transform to extract features from epileptic EEG signals and utilized metaheuristic algorithms for feature selection [18].