Open AccessSHAFA: sparse hybrid adaptive filtering algorithm to estimate channels in various SNR environments
Author(s) -
Wang Jie,
Yang Jie,
Xiong Jian,
Sari Hikmet,
Gui Guan
Publication year - 2018
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2017.1276
Subject(s) - algorithm , term (time) , computer science , mean squared error , convergence (economics) , norm (philosophy) , function (biology) , constraint (computer aided design) , penalty method , mathematics , mathematical optimization , statistics , biology , physics , geometry , quantum mechanics , evolutionary biology , political science , law , economics , economic growth
The ℓ p ‐norm penalised (LP) normalised least mean square algorithm converges faster than the LP normalised least mean fourth algorithm does, but the latter can achieve better steady‐state performance, particularly in regions with low signal‐to‐noise ratios (SNRs). To simultaneously take advantage of both merits, a sparse hybrid adaptive filtering algorithm is proposed in various SNR environments. Specifically, the authors construct a cost function that uses the statistical error term and sparse penalty term. The first term is designed by a hybrid error function of the second‐ and fourth‐order statistical errors, respectively, and the second term is obtained using a sparse constraint function. The hybrid error term can be easily balanced by a proportional parameter α ∈ 0 , 1 . Moreover, they devise a non‐uniform step size in the proposed algorithm to further balance the convergence speed and estimation error. Simulation results are provided to validate the proposed algorithm in various SNR environments.