Communication Signal Processing Based on Wavelet Transform

This graduation thesis centers on communication signal processing utilizing wavelet transform, a critically important field given the rapid advancements in communication technologies and growing demands for efficient signal processing. Wavelet transform serves as a powerful signal processing tool that decomposes signals into distinct frequency components, making it widely applicable in communication systems. The paper investigates wavelet transform principles and their practical applications, while introducing fundamental communication signal processing techniques with MATLAB implementation examples. Key algorithms include Multi-Resolution Analysis (MRA) using discrete wavelet transforms (DWT) for signal denoising and feature extraction. Implementation typically involves wavelet filter banks through functions like wavedec() for decomposition and waverec() for reconstruction. The study aims to provide comprehensive knowledge and practical understanding, enabling readers to better address challenges in modern communication signal processing through both theoretical foundations and code-based applications.