Abstract
The greedy algorithms are efficient ways in reconstructing the sparse signal. Among all the greedy recovery algorithms for practical compressive sampling(CS), Subspace Pursuit(SP) can offer reliable recovery accuracy with low costs. In this paper, the SP algorithm is optimized by reducing the complexity of the least square(LS) caculation in each iteration. The Optimized SP performs well in LTE channel estimation when compared with the other greedy algorithms. The utilization of Partial Fourier Matrix helps reduce the matrix storage in SP hardware implemention. The matrix inverse caculation is also simplified by taking advatage of the Hermite Toeplitz matrix generated from the Fourier Matrix.
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