Adaptive Filters (BER Performance Analysis)

Adaptive filters play a critical role in various communication systems, particularly when dealing with channel distortion. This article examines several equalizer types based on Bit Error Rate (BER) performance analysis, including linear equalizers, Decision Feedback Equalizers (DFE), and Maximum Likelihood Sequence Estimation (MLSE) equalizers.

In static channel environments, evaluating equalizer performance under passband-empty conditions holds significant importance. Linear equalizers serve as fundamental solutions, typically employing simple tap-weight adjustment strategies to mitigate interference. The Decision Feedback Equalizer (DFE) enhances performance by incorporating feedback paths that utilize previous decision results to eliminate Inter-Symbol Interference (ISI).

The Maximum Likelihood Sequence Estimation (MLSE) equalizer demonstrates optimal performance under ideal channel knowledge conditions, as it exhaustively evaluates all possible symbol sequences to select the optimal solution. However, practical applications often involve imperfect channel state information. The accompanying script demonstrates a basic channel estimation method that, despite inherent errors, showcases MLSE's robustness under non-ideal conditions through probability-based sequence comparisons.

By comparing the BER curves of these equalizers, we can visually assess their differential capabilities in noise suppression and channel distortion compensation, providing theoretical foundations for equalizer selection in practical communication systems. The implementation typically involves MATLAB's communication toolbox functions for filter design and BER calculation through Monte Carlo simulations.