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% The conventional RLS algorithm is illustrated % in the context of the adaptive equalization % experiment 10.5.2 by the m-file crlsequa.m % % Programmed by: Dimitris Manolakis, 1999 % %------

function y = cirshftt(x,m,N) % 长度为 N 的x序列: (时域)作m采样点圆周移位 % ------------------------------------------------------------------- % [y] = cirshftt(x,m,N) % y = 包含圆周移位的输出序列 % x = 长度

function [y] = ovrlpsav(x,h,N) % 用混叠相加法作块卷积 % ---------------------------------------- % [y] = ovrlpsav(x,h,N) % y = 输出序列 % x = 输入序列 % h = 脉冲响应 % N = 块长 % Lenx = length(x); M = length(h); M1

clc; clear ; close all; tic M=16;%阵列的天线数 N=3;%信源数 snap=1000;%快拍数目 L=snap; C=3e8; lamda=0.2; f0=C/lamda; d=0.5*lamda; % k=d/lamda; theta0=5; theta1=20; theta2=40; fs=1000; ts=1/fs; t=

function d = distances(v, m) % DISTANCES Squared euclidean distances (works on vectors of any % dimension) % % D is a vector containing the squared Euclidean distance of V from % each row of M. % %

clear all; N=20000; M=31; d=1; M1=32; %sgma=sqrt() for j=1:N temp=rand; dsource(j)=1+floor(M1*temp); end; mapping=[

% M-file for Example 1-16; plots a sample-data sinewave % del_t = 0.2; T0 = 2; n = 0:10; x = sin(2*pi*n*del_t/T0); stem(n*del_t,x), xlabel('n*del_t'), ylabel('x(n*del_t)')

function [W, e, Lambda] = dct_lms_C(u, d, M, alpha, beta, gamma, verbose) % function [W, e, Lambda] = dct_lms_C(u, d, M, alpha, beta, gamma, verbose) % % dct_lms_C.m - use DCT-LMS algorithm with recur

function w_black = Blackman(M); % M-point Blackman window % ----------------------- % w_black = Blackman(M); % M1 = M-1; m = [0:1:M1]; w_black = abs(0.42 - 0.5*cos(2*pi*m'/(M1)) + 0.08*cos(4*

function y = cirshftt(x,m,N) % Circular shift of m samples wrt size N in sequence x: (time domain) % ------------------------------------------------------------------- % [y] = cirshftt(x,m,N) % y