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Diya Banerji
Independent Researcher
India
Abstract
Adaptive filtering has become a cornerstone in noise cancellation for communication systems, providing real-time suppression of unwanted interference. This manuscript reviews classical and modern adaptive algorithms—such as the Least Mean Squares (LMS), Normalized LMS (NLMS), Recursive Least Squares (RLS), and affine projection methods—emphasizing their convergence behavior, complexity, and robustness under non-stationary noise conditions. A statistical analysis comparing steady-state mean-square error (MSE) performance of LMS and RLS filters is presented. Methodology involves simulation of a wireless communication channel corrupted by colored noise sources, with performance metrics evaluated over 10,000 Monte Carlo runs. Results demonstrate that RLS achieves faster convergence and lower steady-state error at the cost of higher computational load, while NLMS offers a favorable trade-off. Identified research gaps include adaptive filter operation in highly dynamic multipath channels, low-power implementation for mobile devices, and robustness against impulsive noise. The study concludes by highlighting avenues for next-generation algorithms that balance performance, complexity, and hardware constraints.
Keywords
adaptive filter, noise cancellation, LMS, RLS, affine projection
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