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  • Online Resource  (4)
  • UB Potsdam  (4)
  • HTW Berlin
  • Bose, Tamal  (4)
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  • Online Resource  (4)
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  • UB Potsdam  (4)
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  • 1
    Online Resource
    Online Resource
    Partial Update Least-Square Adaptive Filtering (2014)
    [San Rafael] : Morgan & Claypool Publishers
    Details
    UID:
    gbv_786663200
    Format: 1 Online-Ressource (117 Seiten)
    Edition: Also available in print
    ISBN: 9781627052320
    Series Statement: Synthesis Lectures on Communications #10
    Content: Adaptive 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(N2)) 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
    Content: 2. Background -- 2.1 Basic adaptive filter models -- 2.2 Adaptive filter models -- 2.2.1 System identification -- 2.2.2 Channel equalization -- 2.3 Existing work on partial update adaptive filters -- 2.4 Basic partial update methods -- 2.4.1 Periodic partial update method -- 2.4.2 Sequential partial update method -- 2.4.3 Stochastic partial update method -- 2.4.4 MMax method --
    Content: 1. Introduction -- 1.1 Motivation -- 1.2 Problem statement -- 1.3 Organization of the monograph --
    Content: 3. Partial update CMA-based algorithms for adaptive filtering -- 3.1 Motivation -- 3.2 Review of constant modulus algorithms -- 3.3 Partial update constant modulus algorithms -- 3.3.1 Partial update CMA -- 3.3.2 Partial update NCMA -- 3.3.3 Partial update LSCMA -- 3.4 Algorithm analysis for a time-invariant system -- 3.4.1 Steady-state performance of partial update SDCMA -- 3.4.2 Steady-state performance of partial update dynamic LSCMA -- 3.4.3 Complexity of the PU SDCMA and LSCMA -- 3.5 Simulation, a simple FIR channel -- 3.5.1 Convergence performance -- 3.5.2 Steady-state performance -- 3.5.3 Complexity -- 3.6 Algorithm analysis for a time-varying system -- 3.6.1 Algorithm analysis of CMA1-2 and NCMA for a time-varying system -- 3.6.2 Algorithm analysis of LSCMA for a time-varying system -- 3.6.3 Simulation -- 3.7 Conclusion --
    Content: 4. Partial-update CG algorithms for adaptive filtering -- 4.1 Review of conjugate gradient algorithm -- 4.2 Partial-update CG -- 4.3 Steady-state performance of partial-update CG for a time-invariant system -- 4.4 Steady-state performance of partial-update CG for a time-varying system -- 4.5 Simulations -- 4.5.1 Performance of different PU CG algorithms -- 4.5.2 Tracking performance of the PU CG using the first-order Markov model -- 4.6 Conclusion --
    Content: 5. Partial-update EDS algorithms for adaptive filtering -- 5.1 Motivation -- 5.2 Review of Euclidean direction search algorithm -- 5.3 Partial update EDS -- 5.4 Performance of the partial-update EDS in a time-invariant system -- 5.5 Performance of the partial-update EDS in a time-varying system -- 5.6 Simulations -- 5.6.1 Performance of the PU EDS in a time-invariant system -- 5.6.2 Tracking performance of the PU EDS using the first-order Markov model -- 5.6.3 Performance comparison of the PU EDS with EDS, PU RLS, RLS, PU CG, and CG -- 5.7 Conclusion --
    Content: 6. Special applications of partial-update adaptive filters -- 6.1 Application in detecting GSM signals in a local GSM system -- 6.2 Application in image compression and classification -- 6.2.1 Simulations -- 6.3 Conclusion --
    Content: Bibliography -- Authors' biographies
    Note: Description based upon print version of record , Introduction; Motivation; Problem Statement; Organization of the Monograph; Background; Basic Adaptive Filter Models; Adaptive Filter Models; System Identification; Channel Equalization; Existing Work on Partial Update Adaptive Filters; Basic Partial Update Methods; Periodic Partial Update Method; Sequential Partial Update Method; Stochastic Partial Update Method; MMax Method; Partial Update CMA-based Algorithms for Adaptive Filtering; Motivation; Review of Constant Modulus Algorithms; Partial Update Constant Modulus Algorithms; Partial Update CMA; Partial Update NCMA; Partial Update LSCMA , Algorithm Analysis for a Time-Invariant SystemSteady-State Performance of Partial Update SDCMA; Steady-State Performance of Partial Update Dynamic LSCMA; Complexity of the PU SDCMA and LSCMA; Simulation - A Simple FIR Channel; Convergence Performance; Steady-State Performance; Complexity; Algorithm Analysis for a Time-Varying System; Algorithm Analysis of CMA1-2 and NCMA for a Time-Varying System; Algorithm Analysis of LSCMA for a Time-Varying System; Simulation; Conclusion; Partial-Update CG Algorithms for Adaptive Filtering; Review of Conjugate Gradient Algorithm; Partial-Update CG , Steady-State Performance of Partial-Update CG for a Time-Invariant SystemSteady-State Performance of Partial-Update CG for a Time-Varying System; Simulations; Performance of Different PU CG Algorithms; Tracking Performance of the PU CG Using the First-Order Markov Model; Conclusion; Partial-Update EDS Algorithms for Adaptive Filtering; Motivation; Review of Euclidean Direction Search Algorithm; Partial update EDS; Performance of the Partial-Update EDS in a Time-Invariant System; Performance of the Partial-Update EDS in a Time-Varying System; Simulations , Performance of the PU EDS in a Time-Invariant SystemTracking performance of the PU EDS Using the First-Order Markov Model; Performance Comparison of the PU EDS with EDS, PU RLS, RLS, PU CG, and CG; Conclusion; Special Applications of Partial-Update Adaptive Filters; Application in Detecting GSM Signals in a Local GSM System; Application in Image Compression and Classification; Simulations; Conclusion; Bibliography; Authors' Biographies , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader.
    Additional Edition: ISBN 9781627052313
    Additional Edition: Print version Partial Update Least-Square Adaptive Filtering
    Language: English
    Keywords: Electronic books
    DOI: 10.2200/S00575ED1V01Y201403COM010
    URL: Volltext
    URL: Volltext
    URL: Inhaltstext
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  • 2
    Online Resource
    Online Resource
    Partial update least-square adaptive filtering (2014)
    San Rafael, California 〈1537 Fourth Street, San Rafael, CA 94901 USA〉 : Morgan & Claypool
    Details
    UID:
    gbv_1654610860
    Format: Online Ressource (1 PDF (ix, 105 pages)) , illustrations.
    Edition: Online-Ausg.
    ISBN: 9781627052320
    Series Statement: Synthesis lectures on communications 1932-1708 # 10
    Content: Adaptive 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(N2)) 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
    Note: Part of: Synthesis digital library of engineering and computer science. - Series from website. - Includes bibliographical references (pages 99-103). - Compendex. INSPEC. Google scholar. Google book search. - Title from PDF title page (viewed on May 21, 2014) , System requirements: Adobe Acrobat Reader.
    Language: English
    URL: Volltext
    URL: Inhaltstext
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  • 3
    Online Resource
    Online Resource
    Basic Simulation Models of Phase Tracking Devices Using MATLAB (2010)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_182389853X
    Format: 1 Online-Ressource(XIV, 122 p.)
    Edition: 1st ed. 2010.
    ISBN: 9783031016769
    Series Statement: Synthesis Lectures on Communications
    Content: Introduction -- Basic PLL Theory -- Structures Developed From The Basic PLL -- Simulation Models -- MATLAB Simulations -- Noise Performance Analysis.
    Content: The Phase-Locked Loop (PLL), and many of the devices used for frequency and phase tracking, carrier and symbol synchronization, demodulation, and frequency synthesis, are fundamental building blocks in today's complex communications systems. It is therefore essential for both students and practicing communications engineers interested in the design and implementation of modern communication systems to understand and have insight into the behavior of these important and ubiquitous devices. Since the PLL behaves as a nonlinear device (at least during acquisition), computer simulation can be used to great advantage in gaining insight into the behavior of the PLL and the devices derived from the PLL. The purpose of this Synthesis Lecture is to provide basic theoretical analyses of the PLL and devices derived from the PLL and simulation models suitable for supplementing undergraduate and graduate courses in communications. The Synthesis Lecture is also suitable for self study by practicing engineers. A significant component of this book is a set of basic MATLAB-based simulations that illustrate the operating characteristics of PLL-based devices and enable the reader to investigate the impact of varying system parameters. Rather than providing a comprehensive treatment of the underlying theory of phase-locked loops, theoretical analyses are provided in sufficient detail in order to explain how simulations are developed. The references point to sources currently available that treat this subject in considerable technical depth and are suitable for additional study. Download MATLAB codes (.zip) Table of Contents: Introduction / Basic PLL Theory / Structures Developed From The Basic PLL / Simulation Models / MATLAB Simulations / Noise Performance Analysis.
    Additional Edition: ISBN 9783031005480
    Additional Edition: ISBN 9783031028045
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031005480
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031028045
    Language: English
    DOI: 10.1007/978-3-031-01676-9
    URL: lizenzpflichtig
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  • 4
    Online Resource
    Online Resource
    Basic simulation models of phase tracking devices using MATLAB (2010)
    [San Rafael] : Morgan & Claypool Publishers
    Details
    UID:
    gbv_715251503
    Format: 1 Online-Ressource (xiv, 122 Seiten)
    Edition: Electronic reproduction Available via World Wide Web
    ISBN: 9781608452606
    Series Statement: Synthesis lectures on communications #5
    Content: Includes bibliographical references
    Content: 1. Introduction -- Outline of the book -- A word of warning -- Origins of this synthesis lecture and a reference --
    Content: 2. Basic PLL theory -- Basic phase-lock loop concepts -- Basic PLL model -- Nonlinear PLL phase model -- PLL linear phase model -- PLL order and loop filters -- Steady-state phase errors -- Acquisition and phase plane analysis -- First-order PLL -- The perfect second-order phase lock loop -- The imperfect second-order phase lock loop -- The perfect third-order phase lock loop -- Transport delay in phase-lock loops -- Problems --
    Content: 3. Structures developed from the basic PLL -- The Costas phase-locked loop -- The QPSK loop -- The N-phase tracking loop -- Problems --
    Content: 4. Simulation models -- Basic models for phase-locked loops -- The simulation model for the Costas PLL -- The QPSK loop -- The N-phase tracking loop -- Error sources in simulation -- Problems --
    Content: 5. MATLAB simulations -- Simulation structure -- Assumed loop inputs -- MATLAB and SIMULINK simulations -- Second-order PLL demonstrations -- QPSK loop -- The N-phase tracking loop -- Problems --
    Content: 6. Noise performance analysis -- PLL with additive noise -- Linear analysis -- Noise bandwidth -- Signal to noise ratio of the loop -- Nonlinear analysis -- PLL with VCO phase noise -- Linear analysis of VCO phase noise -- Simulation of 1st-order PLL with additive noise -- Simulation model -- Simulation results -- Problems --
    Content: A. Complex envelope and phase detector models -- A.1. Complex envelope -- A.2. Phase detector realizations --
    Content: B. Loop filter implementations -- B.1. Trapezoidal integration -- B.2. The loop filter for the perfect second-order phase-locked loop -- B.3. The loop filter for the imperfect second-order phase-locked loop -- B.4. The perfect third-order loop filter --
    Content: Bibliography -- Authors' biographies
    Content: C. SIMULINK examples -- C.1. The perfect second-order PLL -- C.2. The perfect second-order PLL with transport delay -- C.3. The perfect third-order PLL -- C.4. Comments --
    Content: D. MATLAB and SIMULINK files -- D.1. MATLAB files -- D.2. SIMULINK files --
    Note: Description based upon print version of record , Preface; Introduction; Outline of the Book; A Word of Warning; Origins of this Synthesis Lecture and a Reference; Basic PLL Theory; Basic Phase-Lock Loop Concepts; Basic PLL Model; Nonlinear PLL Phase Model; PLL Linear Phase Model; PLL Order and Loop Filters; Steady-State Phase Errors; Acquisition and Phase Plane Analysis; First-order PLL; The Perfect Second-Order Phase Lock Loop; The Imperfect Second-Order Phase Lock Loop; The Perfect Third-Order Phase Lock Loop; Transport Delay in Phase-Lock Loops; Problems; Structures Developed From The Basic PLL; The Costas Phase-Locked Loop , The Perfect Second-Order PLLThe QPSK Loop; The Perfect Second-Order PLL with Transport Delay; The N-Phase Tracking Loop; The Perfect Third-Order PLL; Problems; Comments; Simulation Models; Basic Models for Phase-Locked Loops; The Simulation Model for the Costas PLL; The QPSK Loop; The N-Phase Tracking Loop; Error Sources in Simulation; Problems; MATLAB Simulations; Simulation Structure; Assumed Loop Inputs; MATLAB and SIMULINK Simulations; Second-order PLL Demonstrations; QPSK Loop; The N-Phase Tracking Loop; Problems; Noise Performance Analysis; PLL with Additive Noise; Linear Analysis , Noise BandwidthSignal to Noise Ratio of the Loop; Nonlinear Analysis; PLL with VCO Phase Noise; Linear Analysis of VCO Phase Noise; Simulation of 1st-Order PLL with Additive Noise; Simulation model; Simulation results; Problems; Complex Envelope and Phase Detector Models; Complex Envelope; Phase Detector Realizations; Loop Filter Implementations; Trapezoidal Integration; The Loop Filter for the Perfect Second-Order Phase-Locked Loop; The Loop Filter for the Imperfect Second-Order Phase-Locked Loop; The Perfect Third-Order Loop Filter; SIMULINK Examples; MATLAB and SIMULINK Files; MATLAB Files , SIMULINK FilesBibliography; Authors' Biographies; , Electronic reproduction Available via World Wide Web , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader.
    Additional Edition: ISBN 9781608452590
    Additional Edition: Erscheint auch als Druck-Ausgabe Basic Simulation Models of Phase Tracking Devices Using MATLAB Theory and Applications
    Language: English
    Keywords: Electronic books
    DOI: 10.2200/S00245ED1V01Y201009COM005
    URL: Volltext
    URL: Volltext
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    UB Potsdam
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