Improved least mean square algorithm with application to adaptive sparse channel estimation
نویسندگان
چکیده
منابع مشابه
Improved least mean square algorithm with application to adaptive sparse channel estimation
Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods have been proposed based on different sparse penalties, such as l1-norm LMS or zeroattracting LMS (ZA-LMS), reweighted ZA-LMS, and lp-norm LMS. However, th...
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Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., L1-norm LMS or zero-attracting LMS (sparse LMS or ZA-LMS), reweighted zero attracting LMS (RZA-LMS) and Lp-norm LMS (LP-LMS), have been proposed b...
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Broadband signal transmission over frequencyselective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation methods is least mean square (LMS) algorithm. However, LMS-based method is often degraded by random scaling of input training signal. To improve the estimation performance, in this paper we apply the standard l...
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ژورنال
عنوان ژورنال: EURASIP Journal on Wireless Communications and Networking
سال: 2013
ISSN: 1687-1499
DOI: 10.1186/1687-1499-2013-204