Channel estimation on intelligent reflecting surface based on compressed sensing
A sparse adaptive channel estimation algorithm based on compressed sensing is presented to estimate sparse unknown channels in intelligent reflecting surface(IRS)assisted communication systems.First,the relationship between the l2 norm of the channel residual under the orthogonal matching pursuit(OMP)algorithm and the sparsity of the channel is studied,and the iteration termination conditions for the OMP to restore the sparsity of unknown channels are obtained.Then,a two-stage sparse adaptive channel estimation algorithm is presented.In the first stage,the channel sparseness is estimated,and in the second stage,the supporting set atoms are added or deleted to minimize the channel vector errors recovered.The simulation results show that the performance and robustness of the proposed algorithm are better than those of classical least squares,OMP with known sparsity,and sparse adaptive matching pursuit(SAMP).
NETL
NSTL
万方数据
