Abstract
The article provides an overview of modern noise cancellation technologies in wireless communication with a focus on methods that ensure high-quality information transmission in unstable radio frequency environments. Both traditional adaptive filtering algorithms (e.g., LMS) and advanced neural network-based solutions, including the RNNoise algorithm, are considered. Special attention is paid to hybrid approaches that combine the advantages of classical and intelligent signal processing methods. Simulation modeling using MATLAB was carried out, within which the effectiveness of various algorithms under impulsive, fluctuation, and Gaussian noise was analyzed. Evaluation was performed using SNR and MSE metrics across a wide range of conditions. The results showed that hybrid systems with neural network architectures provide the best noise suppression and demonstrate high adaptability to changing transmission conditions. The conclusion is drawn about the prospects of integrating intelligent algorithms into next-generation wireless communication infrastructure, including 5G and Wi-Fi 6E