Low Complexity Sparse Channel Estimation Based on Compressed Sensing

Abstract: In wireless communication, channel estimation is a key technology to receive signal precisely. Recently, a new method named compressed sensing (CS) has been proposed to estimate sparse channel, which greatly improves the spectrum efficiency. However, it is difficult to realize it due to its high computational complexity. Although the proposed Orthogonal Matching Pursuit (OMP) can reduce the complexity of CS, the efficiency of OMP is still low because only one index is identified per iteration. Therefore, to solve this problem, more efficient schemes are proposed. At first, we apply Generalized Orthogonal Matching Pursuit (GOMP) to channel estimation, which lower computational complexity byselecting multiple indices in each iteration. Then a more effective scheme that selects indices by leastsquares (LS) method is proposed to significantly reduce the computational complexity, which is a modifiedmethod of GOMP. Simulation results and theoretical analysis show the effectivity of the proposed algorithms.
Keywords: channel estimation, compressed sensing, computational complexity, index, atom
Author: Fei Zhou, Yantao Su, Xinyue Fan
Journal Code: jptkomputergg160257

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