TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Novel Adaptive Distributed Compressed Sensing Algorithm for Estimating Channels in Doubly-Selective Fading OFDM Systems |
| 영문제목(English Title) |
Novel Adaptive Distributed Compressed Sensing Algorithm for Estimating Channels in Doubly-Selective Fading OFDM Systems |
| 저자(Author) |
Yuming Song
Xueyun He
Guan Gui
Yan Liang
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| 원문수록처(Citation) |
VOL 13 NO. 05 PP. 2400 ~ 2413 (2019. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Doubly-selective (DS) fading channel is often occurred in many orthogonal frequency division multiplexing (OFDM) communication systems, such as high-speed rail communication systems and underwater acoustic (UWA) wireless networks. It is challenging to provide an accurate and fast estimation over the doubly-selective channel, due to the strong Doppler shift. This paper addresses the doubly selective channel estimation problem based on complex exponential basis expansion model (CE-BEM) in OFDM systems from the perspective of distributed compressive sensing (DCS). We propose a novel DCS-based improved sparsity adaptive matching pursuit (DCS-IMSAMP) algorithm. The advantage of the proposed algorithm is that it can exploit the joint channel sparsity information using dynamic threshold, variable step size and tailoring mechanism. Simulation results show that the proposed algorithm achieves 5dB performance gain with faster operation speed, in comparison with traditional DCS-based sparsity adaptive matching pursuit (DCS-SAMP) algorithm.
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| 키워드(Keyword) |
distributed compressive sensing (DCS)
adaptive matching pursuit
doubly-selective fading channel
basis expansion model (BEM)
channel estimation.
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| 파일첨부 |
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