Application of Wavelet Decomposition in Signal Recognition

In this article, we explore the application of wavelet decomposition in signal recognition. Wavelet decomposition is a signal processing technique commonly employed to analyze various signal types, including single-carrier signals, OFDM signals, and another multicarrier signal called WPM (Wavelet Packet Modulation). By leveraging wavelet decomposition, we can effectively analyze and identify signals while extracting valuable information from them. For implementation, discrete wavelet transform (DWT) algorithms can be applied using functions like MATLAB's `wavedec` for decomposition and `waverec` for reconstruction. Key parameters such as wavelet type (e.g., Daubechies, Haar) and decomposition levels must be optimized based on signal characteristics. For multicarrier signals like OFDM and WPM, wavelet packet decomposition (using `wpdec` in MATLAB) provides finer frequency resolution compared to traditional Fourier-based methods. This technique is particularly beneficial for professionals engaged in signal processing and recognition, as mastering wavelet decomposition enables improved feature extraction, noise reduction, and signal classification accuracy through multi-resolution analysis.