ADAPTIVE FILTERING ALGORITHMS FOR NOISE SUPPRESSION: A PERFORMANCE EVALUATION IN COMMUNICATION NETWORKS

Abstract

Noise significantly degrades the quality and reliability of communication systems. Adaptive noise reduction techniques are widely used to mitigate noise effects in timevarying environments. This paper presents a comprehensive performance evaluation of adaptive noise reduction techniques in communication systems. Popular adaptive algorithms such as LMS, NLMS, and RLS are analyzed and compared. The study focuses on signal quality improvement, convergence behavior, and computational efficiency. Adaptive filtering enables real-time noise suppression under changing conditions. Experimental analysis is conducted using standard performance metrics. Results demonstrate that advanced adaptive techniques outperform basic methods. The comparative evaluation highlights strengths and limitations of each algorithm. The findings support algorithm selection for practical communication systems. Overall, the study provides insights into efficient adaptive noise reduction strategies.