Add example (From lyh)

2024-07-16 15:41:44 +08:00
parent b48ab07dd0
commit 363145afe7
4 changed files with 129 additions and 0 deletions
@@ -0,0 +1,48 @@
+close all;
+clear all;
+clc;
+M = 4;
+N = 128;
+%block_sparsity = 1;
+tol = 1e-5;
+trial = 20;
+epi = 0.02;
+result = zeros(N,25);
+for col = 4:N
+    for block_sparsity = 10:18
+        success_count = 0;
+        for loop = 1:trial
+            FAR_model = zeros(N,M*N);
+            %Cn = randperm(M)-1
+            for n = 0 : N-1
+                Cn = floor(rand()*M);
+                for q = 0 : N-1
+                    for p = 0:M-1
+                        FAR_model(n+1,q*M+p+1) = exp(1i*2*pi*p/M*Cn+1i*2*pi*q/N*n*(1+Cn*epi));
+                    end
+                end
+            end
+            col_choose = randperm(N,col);
+            FAR_model = FAR_model(col_choose,:);
+            sparse_signal = zeros(M,N);
+            block = randperm(N,block_sparsity);
+            sparse_signal(:,block) = exp(1i*2*pi*rand(M,block_sparsity));
+            y = FAR_model * sparse_signal(:);
+            cvx_begin
+            variable x(M,N) complex
+            norm21 = 0;
+            for i = 1:N
+                norm21 = norm21 + norm(x(:,i));
+            end
+            minimize(norm21)
+            subject to
+            FAR_model * x(:) == y
+            cvx_end
+            if norm(x(:)-sparse_signal(:))<tol
+                success_count = success_count+1;
+            end
+        end
+        result(col,block_sparsity) = success_count/trial;
+    end
+end
+save('FARblockepsilon2.mat');
@@ -0,0 +1,47 @@
+close all;
+clear all;
+clc;
+M = 4;
+N = 128;
+%block_sparsity = 1;
+tol = 1e-5;
+trial = 50;
+epi = 0.02;
+result = zeros(N,25);
+for col = 4:4:128
+    for block_sparsity = 1:25
+        success_count = 0;
+        for loop = 1:trial
+            FAR_model = zeros(N,M*N);
+            %Cn = randperm(M)-1
+            for n = 0 : N-1
+                Cn = floor(rand()*M);
+                for q = 0 : N-1
+                    for p = 0:M-1
+                        FAR_model(n+1,q*M+p+1) = exp(1i*2*pi*p/M*Cn+1i*2*pi*q/N*n*(1+Cn*epi));
+                    end
+                end
+            end
+            
+            col_choose = randperm(N,col);
+            FAR_model = FAR_model(col_choose,:);
+            sparse_signal = zeros(M,N);
+
+            block = randperm(N,block_sparsity);
+            sparse_signal(:,block) = exp(1i*2*pi*rand(M,block_sparsity));
+            
+            y = FAR_model * sparse_signal(:);
+            cvx_begin
+            variable x(M*N) complex
+            minimize(norm(x,1))
+            subject to
+            FAR_model * x == y
+            cvx_end
+            if norm(x-sparse_signal(:))<tol
+                success_count = success_count+1;
+            end
+        end
+        result(col,block_sparsity) = success_count/trial;
+    end
+end
+save('FARepsilon.mat');
@@ -0,0 +1,7 @@
+function n = theoretic(m,s,d)
+    syms t;
+    syms u;
+    f = s*(m+t^2)+(d-s)*int((u-t)^2*u^(m-1)*exp(-u^2/2)/(2^(m/2-1)*gamma(m/2)),u,t,inf);
+    g = diff(f,t);
+    t1 = solve(g);
+    n = s*(m+t1^2)+(d-s)*int((u-t1)^2*u^(m-1)*exp(-u^2/2)/(2^(m/2-1)*gamma(m/2)),u,t1,inf);
@@ -0,0 +1,27 @@
+N = 100;
+gauss_phase_res = zeros(100,100);
+for col = 1:100
+    %¾ØÕóÉú³É
+    for p =1:100
+        suc = 0;
+        for loop = 1:50
+            x1 = zeros(N,1);
+            q = randperm(N,p);
+            x1(q) = randn(p,1);
+            fai = randn(col,N);
+            b = fai*x1;
+            cvx_begin quiet
+                variable x(N)
+                minimize( norm( x, 1 ) )
+                subject to
+                    fai * x == b
+            cvx_end
+            %disp((norm(x-x1,1)))
+            if (norm(x-x1,1))<10e-5
+                 suc = suc+1;
+             end
+        end
+        gauss_phase_res(col,p)=suc/50;
+    end
+end
+save gauss_phase_real;