Principle Block Diagram of Linear Adaptive Equalizer
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Resource Overview
This study considers a linear adaptive equalizer's principle block diagram, referenced from the adaptive equalizer application schematic on page 275 of "Introduction to Modern Digital Signal Processing". The implementation utilizes the LMS (Least Mean Squares) algorithm to achieve adaptive equalization, featuring convergence curves of squared errors from single experiments and final filter coefficients. Key implementation aspects include a 500-sample training sequence length, 20 independent trials for statistical analysis, and performance comparison across three distinct step-size parameters to evaluate convergence characteristics.
Detailed Documentation
This paper examines the principle block diagram of a linear adaptive equalizer, corresponding to the application schematic illustrated on page 275 of "Introduction to Modern Digital Signal Processing". The system is implemented using the LMS algorithm, which continuously adjusts filter coefficients through gradient descent optimization. The implementation generates convergence curves plotting squared error versus iteration number for individual experiments, alongside the final optimized filter coefficients. Each experiment employs a 500-sample training sequence for weight adaptation. We conduct 20 independent trials to analyze statistical convergence behavior, with results visualized through superimposed learning curves. Furthermore, we compare convergence performance across three different step-size values (μ parameters) to demonstrate their impact on convergence speed and steady-state error, providing critical insights for practical parameter selection in adaptive filtering applications.
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