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JACIII Vol.21 No.1 pp. 153-158
doi: 10.20965/jaciii.2017.p0153
(2017)

Paper:

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Compressive Sensing-Based Adaptive Sparse Multipath Channel Estimation

Beiyi Liu*, Guan Gui**, Shin-ya Matsushita*, and Li Xu*

*Department of Electronics and Information Systems, Akita Prefectural University
84-4 Tsuchiya-Ebinokuchi, Honjo, Akita 015-0055, Japan
E-mails: {m17b018, matsushita, xuli}@akita-pu.ac.jp

**College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications
No.66, New Mofan Rd., Gulou District, Nanjing, China

Received:
July 5, 2016
Accepted:
October 23, 2016
Published:
January 20, 2017
Creative Commons License
Keywords:
sparse multipath channel estimation, least mean square, ℓ0-LMS, adaptive filter, compressive sensing
Abstract
Sparse multipath channel estimation has recently attracted significant attention due to the sparsity of the channel in broadband wireless communication. Many algorithms have been proposed for sparse multipath channel estimation. Among them, the least mean square (LMS) algorithm, based on adaptive filter, has attracted much attention due to its low complexity and high robustness. However, LMS is usually degraded by the long training signal, which needs large storage space. This paper proposes an improved method that transmits a circulating, short training signal, samples the received signal at a lower rate, and utilizes LMS with ℓ0-norm (ℓ0-LMS) to estimate the sparse multipath channel. This method can achieve high robustness in additive white Gaussian noise (AWGN), and reduce the sampling rate while needing small storage space for the training signal. Numerical simulations are provided to evaluate the performance of the proposed method.
Cite this article as:
B. Liu, G. Gui, S. Matsushita, and L. Xu, “Compressive Sensing-Based Adaptive Sparse Multipath Channel Estimation,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 153-158, 2017.
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