Classical Power Spectrum Estimation and Adaptive Equalization Algorithms

1. The folder contains all programs for two experiments: classical power spectrum estimation and adaptive equalization algorithms. 2. R.m, LMS.m, and LMSmain.m constitute the adaptive equalization algorithm programs: R.m calculates the autocorrelation matrix and eigenvalues of input signals, implementing matrix operations for signal analysis; LMS.m executes the time-domain Least Mean Squares (LMS) algorithm, utilizing statistical simulation methods to generate learning curves under identical channel parameters with varying step sizes, demonstrating convergence behavior; LMSmain.m serves as the main experimental program that processes specific experimental data to produce results and graphical curves. 3. functionx.m, fzhouqitu.m, spectrum.m, bt.m, bart_lett.m, welch.m, and SPECTRUMmain.m form the classical spectrum estimation programs: functionx.m generates functions requiring spectral estimation; fzhouqitu.m computes signal periodograms using Fourier transform methods; spectrum.m performs spectrum estimation using the periodogram method; bt.m implements spectrum estimation through the Blackman-Tukey (BT) method with correlation windowing; bart_lett.m applies Bartlett's method for spectrum estimation using data segmentation; welch.m employs Welch's method combining periodogram averaging with overlapping segments; SPECTRUMmain.m is the main program that processes experimental data to generate results and curves according to requirements. 4. Additionally, supplementary functions such as data processing utilities and plotting tools can be added based on experimental needs to enhance the experimental process and results analysis.