Research on the Efficiency of Communication Devices Based on Adaptive Filtering Algorithms

This paper investigates the optimization of commun ication device efficiency through adaptive filtering algorithms, foc using on improving signal quality, reducing bit error rate (BER), and enhancing spectral efficiency in noisy communication channe ls. Three classic adaptive filtering algorithms—Least Mean Squar e (LMS), Normalized LMS (NLMS), and Recursive Least Squares (RLS)—are systematically analyzed and compared in terms of co nvergence speed, computational complexity, and BER performan ce. Mathematical models for adaptive channel equalization and n oise cancellation are derived, and extensive simulations are condu cted under additive white Gaussian noise (AWGN) and multipath fading channels. Experimental results demonstrate that the RLS algorithm achieves the fastest convergence (150 iterations) with a BER at an SNR of $\mathbf{1 5 ~ d B}$, while the NLMS algorithm balances effi ciency and performance with a $30 \%$ reduction in computational c omplexity compared to RLS. The proposed framework provides a theoretical and practical basis for selecting adaptive filtering algo rithms in resource-constrained communication devices.