Delete useless code

Macroscopic Analysis of VAMP in a MMS/cVAMP.m

@@ -1,77 +0,0 @@
-% Input:y,A,lambda,tau,Kit
-% Output:x_hat_wl,x_hat_d
-
-function [x_hat_wl, x_hat_d] = cVAMP(y, A, lambda, tau, Kit)
-    % Initialization
-    gamma = 768 ./ 1024;
-    k = 0;
-    p = ctranspose(A) * y;
-    h_1 = p;
-    Q_1 = gamma;
-    tau_d = 1;
-
-    % while
-    while (k < Kit) && (tau_d > tau)
-        % Factorized Part
-        x_1 = ST(h_1, lambda, Q_1); % \hat{x}_1^{(k)}
-        chi_1 = F1(x_1, lambda, Q_1); % \chi_1^{(k)}
-        % Message Passing
-        h_2 = x_1 ./ chi_1 - h_1; % h_2^{(k)}
-        Q_2 = 1 ./ chi_1 - Q_1; % \hat{Q}_2^{(k)}
-        % Gaussian Part
-        t1 = (p + h_2) ./ Q_2;
-        t2 = ctranspose(A) * (A * (p + h_2)) / ((Q_2 + 1) * Q_2);
-        x_2 = t1 + t2; % \hat{x}_2^{(k)}
-        chi_2 = gamma ./ (Q_2 + 1) + (1 - gamma) ./ Q_2;
-        % Message Passing
-        h_1_next = x_2 ./ chi_2 - h_2;
-        Q_1_next = 1 ./ chi_2 - Q_2;
-        tau_d = norm(h_1_next - h_1) ./ norm(h_1_next);
-        k = k + 1;
-        % output
-        x_hat_wl = x_1;
-        x_hat_d = h_1_next ./ Q_1_next;
-        % next
-        h_1 = h_1_next;
-        Q_1 = Q_1_next;
-    end
-
-end
-
-% SoftThreshold function
-function x = ST(h_1, lambda, Q_1)
-    [N, M] = size(h_1);
-    x = zeros(N, M);
-
-    for i = 1:N
-        % sign = h_1(i) ./ abs(h_1(i));
-        diff = abs(h_1(i)) - lambda(i);
-        x(i) = sign(h_1(i)) .* (diff ./ Q_1) .* SF(diff);
-    end
-
-end
-
-% Heaviside's step function
-function v = SF(a)
-    % if a > 0
-    %     v = 1;
-    % elseif a == 0
-    %     v = 0;  % at zero points
-    % else
-    %     v = 0;
-    % end
-    v = heaviside(a);
-end
-
-% SoftThreshold function
-function v = F1(x_1, lambda, Q_1)
-    [N, M] = size(x_1);
-    count = 0;
-
-    for i = 1:N
-        temp = Q_1 .* abs(x_1(i)) + lambda(i);
-        count = count + (2 - lambda(i) ./ temp) .* SF(abs(x_1(i)));
-    end
-
-    v = count ./ (2 .* N .* Q_1);
-end

Macroscopic Analysis of VAMP in a MMS/demo1.m

@@ -1,46 +0,0 @@
-% Input:y,A,lambda,tau,Kit
-% Output:x_hat_wl,x_hat_d
-clear;
-clc;
-% rng(1); % 随机种子
-
-%% test_稀疏向量
-% 设定稀疏度
-k = 100; % 设定稀疏度
-
-% 构造感知矩阵D
-m = 768; % 感知矩阵行数
-n = 1024; % 感知矩阵列数 （n>>m）
-
-% D = randn(m,n);     % 生成满足高斯分布的感知矩阵 64*256
-
-F = dftmtx(n);
-row_indices = randperm(n, m);
-D = F(row_indices, :);
-
-% 构造稀疏信号X——共n个元素，其中k个元素不为0
-X = zeros(n, 1);
-index = randperm(n, k);
-val = randn(1, k);
-X(index) = val;
-
-% 得到观测矩阵（压缩后）
-A = D * X;
-
-%%
-% % 通过cVMAP算法完成恢复X，得到恢复后信号
-lambda = ones(n, 1) ./ 10;
-[x_hat_wl, x_hat_d] = cVAMP(A, D, lambda, 1e-4, 200);
-
-%%
-% 显示结果
-figure;
-subplot(3, 1, 1)
-stem(X);
-title('origin signal')
-subplot(3, 1, 2)
-stem(x_hat_wl);
-title('restored signal')
-subplot(3, 1, 3)
-stem(X - x_hat_wl);
-title('differ')

Random PRI/Main.m

@@ -1,41 +0,0 @@
-clc; clear; close all;
-
-parameters;
-
-global trail_times;
-
-[s_T_narrow, A] = narrow_signal_model(t, 1);
-
-A = A / 10;
-
-AH = A';
-
-H0 = [];
-for T = 1: trail_times
-    x = zeros(10, 1);
-    % x(4) = 0.8;
-    y = A * x;
-    y_noise = awgn(y, 15);
-    
-    x_hat = AH * y_noise;
-
-    H0 = [H0 x_hat'];
-end
-
-H1 = [];
-for T = 1: trail_times
-    x = zeros(10, 1);
-    x(4) = 0.8;
-    y = A * x;
-    y_noise = awgn(y, 15);
-    
-    x_hat = AH * y_noise;
-
-    H1 = [H1 x_hat(4)];
-end
-
-% figure; histfit(real(H0));
-% figure; histfit(real(H1));
-
-figure;
-plot()

Random PRI/narrow_signal_model.m

@@ -1,25 +0,0 @@
-function [s_T_narrow, A_narrow] = narrow_signal_model(t, betas_narrow)
-    global f_c N_narrow N FIGURE N_wide numP f_s K_chirp T_r;
-
-    tt = 0: 1/f_s: numP * T_r - 1/f_s;
-
-    % 使用等效散射系数生成窄带情况信号模型
-    s_T_narrow = zeros(1, N * numP);
-    for j = 1: numP
-        idx = ((j-1) * N + (1:N_narrow));
-        tp = idx / f_s;
-        s_T_narrow(1, idx) = exp(1j * 2 * pi * (f_c * tp + 0.5 * K_chirp * tp .^ 2));
-    end
-
-    % s_T_narrow = exp(1j .* 2 .* pi .* f_c .* t);
-    s_R_narrows = zeros(N_narrow, N * numP);
-    for i = 1: N_narrow
-        p = i * (N_wide / N_narrow);
-        s_R_narrows(i, :) = betas_narrow * [zeros(1, p), s_T_narrow(1: end-p)];
-    end
-    
-    A_narrow = s_R_narrows';
-    if FIGURE
-        subplot(2, 1, 1); plot(tt, real(s_T_narrow));
-        subplot(2, 1, 2); plot(tt, real(s_R_narrows(1, :)));
-    end

Random PRI/parameters.m

@@ -1,59 +0,0 @@
-global bandwidth T_p f_s T_s K_chirp f_c PRF T_r numP t tP c N M R_0...
-    delta_R_wide delta_R_narrow N_wide N_narrow ranges_wide...
-    ranges_narrow betas_wide FIGURE K_wide K_narrow trail_times...
-    method N_high TEST lambda tau iter_max noise_sigma FAR_N FAR_M noise_sigma_2 DEBUG_LEVEL...
-;
-
-f_c = 10e9; % X wave
-bandwidth = 100e6;
-T_p = 1e-5;
-f_s = 2 * bandwidth;
-T_s = 1 / f_s;
-K_chirp = bandwidth / T_p;
-T_r = T_p * 10;
-PRF = 1 / T_r;
-
-numP = 10;
-
-t = 0: 1 / f_s: T_r - 1 / f_s;
-tP = 0: 1 / f_s: T_r * numP - 1 / f_s;
-
-c = 3e8;        % 光速
-
-N = length(t);
-N_high = T_p * f_s;
-M = round(T_p * bandwidth);
-R_0 = 0;
-delta_R_wide = c ./ 2 ./ bandwidth;
-delta_R_narrow = T_p .* c ./ 2;
-N_wide = 100;
-N_narrow = round(N_wide / M);
-
-% alert(N > N_narrow);
-
-ranges_wide = R_0 + (0:N_wide) * delta_R_wide; % [1000, 4000]
-ranges_narrow = R_0 + (0:N_narrow) * delta_R_narrow; % [1000, 4000]
-betas_wide = linspace(1, 0.1, N_wide) + 1j * linspace(0.1, 1, N_wide);
-
-K_wide = round(N_wide / 10);
-K_narrow = round(K_wide / 10);
-
-FIGURE = false;
-trail_times = 5000;
-% method = "debiased_LASSO";
-% method = "LASSO";
-% method = "BP";
-method = "cVAMPro";
-TEST = false;
-
-lambda = 0.01;
-tau = 1e-6;
-iter_max = 1000;
-
-noise_sigma = 0.1;
-noise_sigma_2 = 0.1;
-
-FAR_N = 480;
-FAR_M = 10;
-
-DEBUG_LEVEL = 1;

Randomized Stepped FR Exploiting BS of ET/test.m

@@ -1,17 +0,0 @@
-N = 32e9;
-M = 3;
-eps = 1e-4;
-
-delta_1 = 24 * sqrt((M-1)/N) * log(M*N) * (2*sqrt(log(M * N) - log(eps)) + 1);
-delta_2 = 3/2 * sqrt((M-1)/N) * (2*sqrt(log(M * 2) - log(eps)) + 1);
-
-K = N * (1/8 - delta_1 - delta_2)^2 / (81 * M * log(M * N) * (1 + 2/3 * delta_2));
-
-x = 0:25;
-Ns = N * (1+randn(size(x)));
-delta_1 = 24 * sqrt((M-1)./N) * log(M.*N) * (2*sqrt(log(M .* N) - log(eps)) + 1);
-delta_2 = 3/2 * sqrt((M-1)./N) * (2*sqrt(log(M * 2) - log(eps)) + 1);
-Ks = Ns * (1/8 - delta_1 - delta_2)^2 / (81 * M * log(M .* Ns) * (1 + 2/3 * delta_2));
-
-rate = K .* M .* log(M .* Ns) ./ Ns;
-plot(x, rate)