Abstract
Wireless communication requires accurate Channel State Information (CSI) for coherent detection. Due to the broadband signal transmission, dominant channel taps are often separated in large delay spread and thus are exhibited highly sparse distribution. Sparse Multi-Path Channel (SMPC) estimation using Orthogonal Matching Pursuit (OMP) algorithm has took advantage of simplification and fast implementation. However, its estimation performance suffers from large Mutual Incoherent Property (MIP) interference in dominant channel taps identification using Random Training Matrix (RTM), especially in the case of SMPC with a large delay spread or utilizing short training sequence. In this study, we propose a MIP mitigation method to improve sparse channel estimation performance. To improve the estimation performance, we utilize a designed Sensing Training Matrix (STM) to replace with RTM. Numerical experiments illustrate that the improved estimation method outperforms the conventional sparse channel methods which neglected the MIP interference in RTM.
Highlights
In the last decades, how to overcome the scarcity of spectral resource to meet the ever-growing need for high data rate was a great challenge for communication engineers
In our previous research (Gui et al, 2011), we have proposed a sensing matrix designing method for sparse signal recovery based on the Orthogonal Matching Pursuit (OMP) algorithm in real-valued domain
We propose a Mutual Incoherent Property (MIP)-mitigated method to improve the performance of Sparse Multi-Path Channel (SMPC) estimation based on the OMP algorithm
Summary
How to overcome the scarcity of spectral resource to meet the ever-growing need for high data rate was a great challenge for communication engineers. According to the sufficient condition developed by (Tropp and Gilbert, 2007; Cai and Wang, 2011), both the suboptimal algorithms (i.e., MP and OMP) suffer from Mutual Incoherent Property (MIP) (Donoho and Huo, 2001), interference due to coherency and redundancy of equivalent training matrix, especially in the case of SMPC with either large time delay spread or relatively small number of training data and received data. In our previous research (Gui et al, 2011), we have proposed a sensing matrix designing method for sparse signal recovery based on the OMP algorithm in real-valued domain.
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