000 05455nam a22005175i 4500
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008 220601s2014 sz | s |||| 0|eng d
020 _a9783031016813
_9978-3-031-01681-3
024 7 _a10.1007/978-3-031-01681-3
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aXie, Bei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985161
245 1 0 _aPartial Update Least-Square Adaptive Filtering
_h[electronic resource] /
_cby Bei Xie, Tamal Bose.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXI, 105 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Communications,
_x1932-1708
505 0 _aIntroduction -- Background -- Partial Update CMA-based Algorithms for Adaptive Filtering -- Partial-Update CG Algorithms for Adaptive Filtering -- Partial-Update EDS Algorithms for Adaptive Filtering -- Special Applications of Partial-Update Adaptive Filters -- Bibliography -- Authors' Biographies .
520 _aAdaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful. This monograph mostly focuses on the analyses of the partial update least-squares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA1-2 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_985163
650 0 _aTelecommunication.
_910437
650 1 4 _aTechnology and Engineering.
_985165
650 2 4 _aElectrical and Electronic Engineering.
_985168
650 2 4 _aCommunications Engineering, Networks.
_931570
700 1 _aBose, Tamal.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985171
710 2 _aSpringerLink (Online service)
_985173
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005534
776 0 8 _iPrinted edition:
_z9783031028090
830 0 _aSynthesis Lectures on Communications,
_x1932-1708
_985174
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01681-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c85776
_d85777