Sparse Recovery Problem Solution
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Hello all,
I am working on a communication system where I physically have Np triplets {hp,taup,ap}. From these Np triplets I can build the exact channel matrix.
In practice however I need to estimate the channel. To estimate the channel I first build the dictionaries tau of cardinality Nt and a of cardinality Na. From these dictionaries I form the equation Ax=z, where z is the noisy observation vector, A=[Gamma1*Lambda1*s ... Gamma1*LambdaNt*s .... GammaNa*Lambda1*s .... GammaNa*LambdaNt*s] and x=[x(1,1) ...x(1,Nt) .... x(Na,Nt)]=[x1 ... x_NaNt]. s is the known pilot symbols.
Obviously, x is sparse, and I am using the Basis Pursuit (BP) algorithm to find it. The problem is that when solving the problem x contains much more that Np non-zero elements. How can I find Np unique triplets from Ax=z? This means there is no two triplets share the same hp, taup or ap?
Thanks
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Matt J
2014-6-30
编辑:Matt J
2014-6-30
Isn't it obvious that A=ones(1,N) and z=1? How were you interpreting your previous A*x=z if not the constraints of an L1 minimization problem?
The solution I would expect is an x of the form
x=zeros(N,1);
x(j)=1;
This is the sparsest solution you can have: only 1 non-zero element.
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