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Signal Processing Magazine, IEEE

Issue 1 • Date Jan. 2004

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Displaying Results 1 - 23 of 23
  • Structured low-density parity-check codes

    Page(s): 42 - 55
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1227 KB) |  | HTML iconHTML  

    This article describes the different methods to design regular low density parity-check (LDPC) codes with large girth. In graph terms, this corresponds to designing bipartite undirected regular graphs with large girth. Large girth speeds the convergence of iterative decoding and improves the performance at least in the high SNR range, by slowing down the onsetting of the error floor. We reviewed several existing constructions from exhaustive search to highly structured designs based on Euclidean and projective finite geometries and combinatorial designs. We describe GB and TS LDPC codes and compared the BER performance with large girth to the BER performance of random codes. These studies confirm that in the high SNR regime these codes with high girth exhibit better BER performance. The regularity of the codes provides additional advantages that we did not explore in this article like the simplicity of their hardware implementation and fast encoding. View full abstract»

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  • Low-density parity check codes for partial response channels

    Page(s): 56 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (740 KB) |  | HTML iconHTML  

    The goals of this article are twofold: (1) to provide a brief tutorial of the application of low-density parity check (LDPC) codes for partial response (PR) channels under the framework of turbo equalization and (2) to highlight the use of structured LDPC codes in PR systems. We begin by introducing LDPC codes, their graph representations and associated sum-product decoding algorithm, followed by describing the general framework of iterative equalization and decoding approach to combat ISI. We then present explicit constructions of structured LDPC codes, which facilitate efficient implementation of encoding and decoding and show simulation results. View full abstract»

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  • Turbo equalization

    Page(s): 67 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (921 KB)  

    Turbo equalization is an iterative equalization and decoding technique that can achieve equally impressive performance gains for communication systems that send digital data over channels that require equalization, i.e., those that suffer from intersymbol interference (ISI). In this article, we discuss the turbo equalization approach to coded data transmission over ISI channels, with emphasis on the basic ideas and some of the practical details. The original system introduced by Douillard et al. can be viewed as an extension of the turbo decoding algorithm by considering the effect of the ISI channel as another form of error protection, i.e., as a rate-1 convolutional code. View full abstract»

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  • Another contender in the arctangent race

    Page(s): 109 - 110
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (277 KB)  

    Fast and accurate methods for computing the arctangent of a complex number x = I + jQ have been the subject of extensive study because estimating the angle θ of a complex value has so many applications in the field of signal processing. Practitioners interested in high-speed arctangent computations typically use look-up tables where the values of I and Q specify an address in read-only memory (ROM) containing an approximation of angle θ. In this paper, another method to compute the arctangent approximation is proposed that uses neither the look-up table nor high order polynomials. View full abstract»

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  • Cyclic minimizers, majorization techniques, and the expectation-maximization algorithm: a refresher

    Page(s): 112 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (250 KB)  

    Many parameter estimation problems in signal processing can be reduced to the task of minimizing a function of the unknown parameters. This task is difficult owing to the existence of possibly local minima and the sharpness of the global minimum. In this article we review three approaches that can be used to minimize functions of the type encountered in parameter estimation problems. The first two approaches, the cyclic minimization and the majorization technique, are quite general, whereas the third one, the expectation-maximization (EM) algorithm, is tied to the use of the maximum likelihood (ML) method for parameter estimation. The article provides a quick refresher of the aforementioned approaches for a wide readership. View full abstract»

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  • Iterative timing recovery

    Page(s): 89 - 102
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1635 KB)  

    The last decade has seen the development of iteratively decodable error-control codes of unprecedented power, whose large coding gains enable reliable communication at very low signal-to-noise ratio (SNR). A by-product of this trend is that timing recovery must be performed at an SNR lower than ever before. Conventional timing recovery ignores the presence or error-control coding and thus doomed to fail when the SNR is low enough. This article describes the iterative timing recovery, a method for implementing timing recovery in cooperation with iterative error-control decoding so as to approximate a more complicated receiver that jointly solves the timing recovery and decoding problems. View full abstract»

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  • New Products

    Page(s): 116
    Save to Project icon | Request Permissions | PDF file iconPDF (196 KB)  
    Freely Available from IEEE
  • Listen to your data [signal processing applications]

    Page(s): 21 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (283 KB)  

    This paper gives emphasis on the importance of testing the performance of signal processing algorithms with real data and learning from the data. The discussion is made primarily within the context of a patient monitoring research. The author suggests six steps on how to test with and learn from real data. These steps are: (1) minimize measurement uncertainty so your physiological phenomenon is consistently observable, (2) understand your data, (3) acquire a significant number of appropriate training and testing sets, (4) construct high-performance validation criteria, (5) design algorithms that can generalize in the clinical environment, and (6) test and learn from your data. Results show that this process is also applicable to other fields as well. View full abstract»

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  • Iterative multiuser detection

    Page(s): 81 - 88
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (648 KB)  

    Communication channels that involve both error-control coding and multiple-access signaling are of increasing interest in applications such as cellular telephony, wireless computer networks, and broadband local access. Optimal data detection and decoding in such channels generally require a level of computational complexity that is prohibitive for these types of applications. Turbo multiuser detection (MUD) addresses this problem by applying turbo principle of iteration among constituent decision algorithms, with intermediate exchanges of soft information about tentative decisions. This principle is applied in this paper by considering MUD and error-control decoding as the two constituent decision algorithms. The resulting iteration between soft MUD and soft channel decoding yields good results. This article reviews this area outlining both the basic principles involved and the basis for low-complexity turbo multiuser detectors that require minimal increased complexity over that of the standard channel decoder. View full abstract»

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  • An Introduction - President's message

    Page(s): 4 - 5
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  • Dates ahead

    Page(s): 119
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  • An introduction to factor graphs

    Page(s): 28 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (909 KB)  

    Graphical models such as factor graphs allow a unified approach to a number of key topics in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes and similar codes, joint decoding, equalization, parameter estimation, hidden-Markov models, Kalman filtering, and recursive least squares. Graphical models can represent complex real-world systems, and such representations help to derive practical detection/estimation algorithms in a wide area of applications. Most known signal processing techniques -including gradient methods, Kalman filtering, and particle methods -can be used as components of such algorithms. Other than most of the previous literature, we have used Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams. View full abstract»

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  • Leadership-some random thoughts

    Page(s): 16 - 20
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  • Society News

    Page(s): 10 - 25
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  • IEEE Signal Processing Magazine

    Page(s): 0_1
    Save to Project icon | Request Permissions | PDF file iconPDF (406 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 1
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    Freely Available from IEEE
  • Bylaw changes

    Page(s): 13 - 15
    Save to Project icon | Request Permissions | PDF file iconPDF (76 KB)  
    Freely Available from IEEE

Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Min Wu
University of Maryland, College Park
United States 

http://www/ece.umd.edu/~minwu/