Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system
Abstract Compressed sensing (CS)-based sparse channel estimation requires the sensing matrix with the minimum mutual coherence (MC), and its corresponding pilot pattern obtain optimal estimation performance. In order to minimize the MC of the sensing matrix, a deterministic optimized pilot pattern a...
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Online Access: | http://link.springer.com/article/10.1186/s13638-018-1331-y |
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doaj-40754fca36554484bf03f0a88210633c2020-11-25T02:07:40ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992019-01-01201911810.1186/s13638-018-1331-yDeterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM systemYang Nie0Xinle Yu1Zhanxin Yang2Engineering Research Centre of Digital Audio and Video Ministry of Education, Communication University of ChinaEngineering Research Centre of Digital Audio and Video Ministry of Education, Communication University of ChinaEngineering Research Centre of Digital Audio and Video Ministry of Education, Communication University of ChinaAbstract Compressed sensing (CS)-based sparse channel estimation requires the sensing matrix with the minimum mutual coherence (MC), and its corresponding pilot pattern obtain optimal estimation performance. In order to minimize the MC of the sensing matrix, a deterministic optimized pilot pattern allocation scheme based on modified adaptive genetic algorithm (MAGA) is investigated in this paper. By adjusting the probability of mutation and crossover adaptively, the proposed scheme guides the search process to obtain the optimized pilot pattern. This method guarantees the convergence of the optimization process and prevents the process into local optimization to get the global optimization. Compared with the existing methods, simulation results prove that the proposed scheme obtain the sensing matrix with the smaller MC, whose corresponding deterministic pilot pattern effectively improve channel estimation performance.http://link.springer.com/article/10.1186/s13638-018-1331-ySparse channel estimationPilot patternMutual coherenceCompressed sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yang Nie Xinle Yu Zhanxin Yang |
spellingShingle |
Yang Nie Xinle Yu Zhanxin Yang Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system EURASIP Journal on Wireless Communications and Networking Sparse channel estimation Pilot pattern Mutual coherence Compressed sensing |
author_facet |
Yang Nie Xinle Yu Zhanxin Yang |
author_sort |
Yang Nie |
title |
Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system |
title_short |
Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system |
title_full |
Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system |
title_fullStr |
Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system |
title_full_unstemmed |
Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system |
title_sort |
deterministic pilot pattern allocation optimization for sparse channel estimation based on cs theory in ofdm system |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1499 |
publishDate |
2019-01-01 |
description |
Abstract Compressed sensing (CS)-based sparse channel estimation requires the sensing matrix with the minimum mutual coherence (MC), and its corresponding pilot pattern obtain optimal estimation performance. In order to minimize the MC of the sensing matrix, a deterministic optimized pilot pattern allocation scheme based on modified adaptive genetic algorithm (MAGA) is investigated in this paper. By adjusting the probability of mutation and crossover adaptively, the proposed scheme guides the search process to obtain the optimized pilot pattern. This method guarantees the convergence of the optimization process and prevents the process into local optimization to get the global optimization. Compared with the existing methods, simulation results prove that the proposed scheme obtain the sensing matrix with the smaller MC, whose corresponding deterministic pilot pattern effectively improve channel estimation performance. |
topic |
Sparse channel estimation Pilot pattern Mutual coherence Compressed sensing |
url |
http://link.springer.com/article/10.1186/s13638-018-1331-y |
work_keys_str_mv |
AT yangnie deterministicpilotpatternallocationoptimizationforsparsechannelestimationbasedoncstheoryinofdmsystem AT xinleyu deterministicpilotpatternallocationoptimizationforsparsechannelestimationbasedoncstheoryinofdmsystem AT zhanxinyang deterministicpilotpatternallocationoptimizationforsparsechannelestimationbasedoncstheoryinofdmsystem |
_version_ |
1724930392933269504 |