1 edition of Iterative Detection found in the catalog.
Iterative Detection: Adaptivity, Complexity Reduction, and Applications is a primary resource for both researchers and teachers in the field of communication. Unlike other books in the area, it presents a general view of iterative detection that does not rely heavily on coding theory or graph theory. The features of the text include: Both theoretical background and numerous real-world applications. Over 70 detailed examples, 100 problems, 180 illustrations, tables of notation and acronyms, and an extensive bibliography and subject index. A whole chapter devoted to a case study on turbo decoder design. Receiver design guidelines, rules and suggestions. The most advanced view of iterative (turbo) detection based only on block diagrams and standard detection and estimation theory. Development of adaptive iterative detection theory. Application of adaptive iterative detection to phase and channel tracking in turbo coded systems and systems representative of digital mobile radio designs. An entire chapter dedicated to complexity reduction. Numerous recent research results. Discussion of open problems at the end of each chapter. Among the applications considered in this book are joint equalization and decoding, turbo codes, multiuser detection and decoding, broadband wireless channel equalization, and applications to two-dimensional storage and imaging systems. Audience: Iterative Detection: Adaptivity, Complexity Reduction, and Applications provides an accessible and detailed reference for researchers, practicing engineers, and students working in the field of detection and estimation. It will be of particular interest to those who would like to learn how iterative detection can be applied to equalization, interference mitigation, and general signal processing tasks. Researchers and practicing engineers interested in learning the turbo decoding algorithm should also have this book.
|Statement||by Keith M. Chugg, Achilleas Anastasopoulos, Xiaopeng Chen|
|Series||The Springer International Series in Engineering and Computer Science -- 602, International series in engineering and computer science -- 602.|
|Contributions||Anastasopoulos, Achilleas, Chen, Xiaopeng|
|The Physical Object|
|Format||[electronic resource] :|
|Pagination||1 online resource (xxvii, 359 pages).|
|Number of Pages||359|
|ISBN 10||1461356849, 1461516994|
|ISBN 10||9781461356844, 9781461516996|
Physical implications of the matrix representation of a detection system are discussed. An iterative method of solution is presented. Proof of convergence to the exact solution is given for two-dimensional, triangular and positive definite type response matrices. Iterative detection and decoding to approach MIMO capacity Jun Won Choi In this term project, I studied on MIMO receiver techniques which can achieve near-capacity performance by applying an iterative detection and decoding (IDD) process. This report will provide the brief summary on what I have learned in this project, which will supplement File Size: KB.
Starting with basic concepts in digital communications, progressively more complex ideas are presented and integrated resulting in the development of cutting-edge algorithms for iterative receivers. Real-world applications are covered in detail, including decoding for turbo and LDPC codes, and detection for multi-antenna and multi-user systems. Eq. shows that if we can get the exact normal of each point, and the scanned data is dense enough, the noise will have little effect on the shape recognition. Though the sc Fig. 2 shows the normal influence on the slippage shape recognition of fandisk model. We use only 1-ring neighborhood of the points to approximate the normal and to compute the slippage motion Cited by:
Find Iterative Detection: Adaptivity, Complexity Reduction, and Applications by Chugg et al at over 30 bookstores. Buy, rent or sell. INSTITUTE OF TELECOMMUNICATIONS Outline 1 Introduction 2 Soft Output Decoding 3 Serially Concatenated Codes (SCC) 4 Parallel Concatenated Codes (PCC) 5 Low-Density Parity Check (LDPC) Codes 6 Iterative Detection 7 Future Trends S. ten BrinkIterative Detection and Decoding/
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Iterative Detection: Adaptivity, Complexity Reduction, and Applications is a primary resource for both researchers and teachers in the field of communication. Unlike other books in the area, it presents a general view of iterative detection that does not.
Iterative Detection: Adaptivity, Complexity Reduction, and Applications is a primary resource for both researchers and teachers in the field of communication.
Unlike other books in the area, it presents a general view of iterative detection that does not Format: Paperback. Coding and Iterative Detection for Magnetic Recording Channels (The Springer International Series in Engineering and Computer Science Book ) - Kindle edition by Wu, Zining.
Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Coding and Iterative Detection for Magnetic Manufacturer: Springer. Iterative Detection | Iterative Detection: Adaptivity, Complexity Reduction, and Applications is a primary resource for both researchers and Iterative Detection book in the field of communication.
Unlike other books in the area, it presents a general view of iterative detection that does not rely heavily on coding theory or graph theory.
Coding and Iterative Detection for Magnetic Recording Channels - Ebook written by Zining Wu. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Coding and Iterative Detection book Detection for Magnetic Recording Channels.
This book is an ideal entry point for exploring ongoing research in multiuser detection and for learning about the field's existing unsolved problems and issues.
It is a valuable resource for researchers, engineers, and graduate students who are involved in the area of digital communications. Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation Book Abstract: Providing an all-encompassing self-contained treatment of Near-Capacity Multi-Functional MIMO Systems, the book starts by categorizing the family of Multiple-Input Multiple-Output (MIMO) schemes as diversity techniques, multiplexing.
View Notes - Chugg_book_Ch1 from EE at University of Southern California. Iterative Detection Adaptivity, Complexity Reduction, and Applications ITERATIVE DETECTION Adaptivity, ComplexityAuthor: Edwardlee. Iterative Detection: Adaptivity, Complexity Reduction, and Applications is a primary resource for both researchers and teachers in the field of communication.
Unlike other books in the area, it presents a general view of iterative detection that does not rely heavily on coding theory or graph theory. The features of the text include:4/5(1). As outlined in the introduction of this book and demonstrated in Chapter 2, iterative detection itself may be viewed as a complexity reduction technique.
In fact, the vast majority of complexity reduction obtained in the examples of this book and chapter results from modeling a given system by: 1. Get this from a library. Coding and Iterative Detection for Magnetic Recording Channels.
[Zining Wu] -- This book is a useful tool for researchers in both academia and industry who are interested in improving the performances of magnetic recording systems using new. Iterative definition is - involving repetition: such as. How to use iterative in a sentence. The dropout function will do iterative looping to test for outliers.
For each element you pass in, it will return 1 if the element is not an outliers, otherwise it will return the p-value book: king's fool bound to him by magic, captive girl is.
The book contains a page chapter on the unified treatment of all block codes in the context of high-flexibility, cutting-edge irregular Linear Dispersion Codes (LDC), which approach the MIMO-capacity.
The majority of the book’s solutions are in the optimum sphere-packing frame-work. 11 Iterative Detection of Channel-coded DSTS Schemes Price: $ An iterative QR-based soft feedback segment interference cancellation (QRSFSIC) detection and decoder algorithm for a Reed-Muller (RM) space-time turbo system is proposed in this paper.
The advent of the internet age has produced enormous demand for in creased storage capacity and for the consequent increases in the amount of information that can be stored in a small space. While physical and media improvements have driven the majority of improvement in modern storage systems,Brand: Springer US.
The proposed iterative scheme, named as backward iterative detection, exploits the tentative decisions on lately detected streams in order to mainly. About the Author. Lajos Hanzo FREng, FIEEE, FIET, DSc received his degree in electronics in and his doctorate in During his year career in telecommunications he has held various research and academic posts in Hungary, Germany and the UK.
In computational mathematics, an iterative method is a mathematical procedure that uses an initial guess to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.A specific implementation of an iterative method, including the termination criteria, is an algorithm of the iterative method.
Unlike most of the previous books on multiuser detection, this book is narrower in scope and covers topics in more depth Tutorial chapters on topics such as fundamental limits, iterative techniques, performance with random signatures, multiuser detection with fading channels, and interference avoidance.
aspect of the vanishing point detection. Our ﬁrst contribu-tion is a non-iterative solution for simultaneously estimat-ing the vanishing points in an image given a set of sparse edges.
Our solution is based on an approach recently pro-posed by Toldo and Fusiello, called J-Linkage . Simi-larly to RANSAC, it estimates vanishing point hypotheses. Iterative Detection: Adaptivity, Complexity Reduction, and Applications (The Springer International Series in Engineering and Computer Science) Report Browse more videos.In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems.
MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor Cited by: 1.