48 lines
1.3 KiB
Matlab
48 lines
1.3 KiB
Matlab
close all;
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clear all;
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clc;
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M = 4;
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N = 128;
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%block_sparsity = 1;
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tol = 1e-5;
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trial = 50;
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epi = 0.02;
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result = zeros(N,25);
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for col = 4:4:128
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for block_sparsity = 1:25
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success_count = 0;
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for loop = 1:trial
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FAR_model = zeros(N,M*N);
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%Cn = randperm(M)-1
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for n = 0 : N-1
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Cn = floor(rand()*M);
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for q = 0 : N-1
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for p = 0:M-1
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FAR_model(n+1,q*M+p+1) = exp(1i*2*pi*p/M*Cn+1i*2*pi*q/N*n*(1+Cn*epi));
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end
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end
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end
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col_choose = randperm(N,col);
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FAR_model = FAR_model(col_choose,:);
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sparse_signal = zeros(M,N);
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block = randperm(N,block_sparsity);
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sparse_signal(:,block) = exp(1i*2*pi*rand(M,block_sparsity));
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y = FAR_model * sparse_signal(:);
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cvx_begin
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variable x(M*N) complex
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minimize(norm(x,1))
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subject to
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FAR_model * x == y
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cvx_end
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if norm(x-sparse_signal(:))<tol
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success_count = success_count+1;
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end
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end
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result(col,block_sparsity) = success_count/trial;
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end
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end
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save('FARepsilon.mat');
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