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E-raamat: LDPC Code Designs, Constructions, and Unification

(University of California, Davis), (University of California, Davis), (University of Notre Dame, Indiana), (University of California, Davis),
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 01-Dec-2016
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316811931
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 01-Dec-2016
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316811931

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"Written by leading experts, this self-contained text provides systematic coverage of LDPC codes and their construction techniques, unifying both algebraic- and graph-based approaches into a single theoretical framework (the superposition construction). An algebraic method for constructing protograph LDPC codes is described, and entirely new codes and techniques are presented. These include a new class of LDPC codes with doubly quasi-cyclic structure, as well as algebraic methods for constructing spatially and globally coupled LDPC codes. Authoritative, yet written using accessible language, this text is essential reading for electrical engineers, computer scientists and mathematicians working in communications and information theory"--

Arvustused

'This book provides an in-depth survey of recently developed quasi-cyclic LDPC codes. It is a treasured reference on practical channel coding methods for both theorists and practitioners working in communications and information theory.' Lara Dolecek, University of California, Los Angeles 'The importance of LDPC codes in numerous applications and their capacity-approaching performance has led to an explosion in research into their construction and analysis over the past decade. The numerous effective constructions of them can be broadly classified as algebraic and graphical, including the important superposition, protograph and spatial coupling techniques. This timely volume explains, unifies and greatly clarifies these diverse approaches and lays a solid foundation that will be invaluable to researchers, practitioners and students alike.' Ian F. Blake, University of British Columbia, Vancouver 'A book from the leaders in the field of error-correcting codes. Superposition - a unified framework for low-density parity check code construction - makes a description of codes of various classes rather simple.' Bane Vasic, University of Arizona, Tucson

Muu info

In this book, leading authorities unify algebraic- and graph-based LDPC code designs and constructions into a single theoretical framework.
Preface viii
1 Introduction 1(5)
2 Definitions, Concepts, and Fundamental Characteristics of LDPC Codes 6(10)
2.1 Matrices and Matrix Dispersions of Finite Field Elements
6(2)
2.2 Fundamental Structural Properties and Performance Characteristics of LDPC Codes
8(6)
2.3 Discussion and Remarks
14(2)
3 A Review of PTG-Based Construction of LDPC Codes 16(5)
3.1 PTG-LDPC Code Construction
16(4)
3.2 Conclusion and Remarks
20(1)
4 An Algebraic Method for Constructing QC-PTG-LDPC Codes and Code Ensembles 21(20)
4.1 Construction of QC-PTG-LDPC Codes by Decomposing Base Matrices
21(5)
4.2 Construction of RC-Constrained PTG Parity-Check Matrices
26(2)
4.3 Examples
28(10)
4.4 Construction of the Ensemble of PTG-LDPC Codes from an Algebraic Point of View
38(2)
4.5 Discussion and Remarks
40(1)
5 Superposition Construction of LDPC Codes 41(10)
5.1 SP-Construction of LDPC Codes and Its Graphical Interpretation
41(2)
5.2 Ensembles of SP-LDPC Codes
43(1)
5.3 Constraints on the Construction of SP-LDPC Codes Free of Cycles of Length
44(4)
5.4 SP-Construction of QC-LDPC Codes
48(1)
5.5 SP-Base Matrices over Nonnegative Integers
49(1)
5.6 Discussion and Remarks
50(1)
6 Construction of Base Matrices and RC-Constrained Replacement Sets for SP-Construction 51(21)
6.1 RC-Constrained Base Matrices
51(2)
6.2 Construction of RC-Constrained Replacement Sets Based on Hamming Codes
53(6)
6.3 Construction of RC-Constrained Replacement Sets Based on m-dimensional Euclidean Geometry EG(m, 2) over GF(2)
59(3)
6.4 Construction of RC-Constrained Replacement Sets Based on RC-Constrained Arrays of CPMs
62(7)
6.5 Discussion and Remarks
69(3)
7 SP-Construction of QC-LDPC Codes Using Matrix Dispersion and Masking 72(26)
7.1 A Deterministic SP-Construction of QC-LDPC Codes
72(1)
7.2 Conditions on Girth of CPM-QC-SP-LDPC Codes
73(1)
7.3 A Finite Field Construction of 2 x 2 SM-Constrained SP-Base Matrices and Their Associated CPM-QC-SP-LDPC Codes
74(3)
7.4 Masking
77(7)
7.5 Design of Masking Matrices
84(8)
7.6 Construction of CPM-QC-SP-LDPC Codes for Correcting Bursts of Erasures by Masking
92(3)
7.7 Discussion and Remarks
95(3)
8 Doubly QC-LDPC Codes 98(13)
8.1 Base Matrices with Cyclic Structure
98(2)
8.2 CPM-D-SP-Construction of Doubly QC-LDPC Codes
100(4)
8.3 Masking and Variations
104(4)
8.4 SP-Construction of CPM-QC-SP-LDPC Codes
108(1)
8.5 Discussion and Remarks
108(3)
9 SP-Construction of Spatially Coupled QC-LDPC Codes 111(27)
9.1 Base Matrices and Their Structural Properties
112(2)
9.2 Type-1 QC-SC-LDPC Codes
114(6)
9.3 Type-2QC-SC-LDPC Codes
120(3)
9.4 Terminated and Tailbiting CPM-QC-SC-LDPC Codes
123(6)
9.5 A More General Construction of Type-1 CPM-QC-SC-LDPC Codes
129(5)
9.6 A More General Construction of Type-2 CPM-QC-SC-LDPC Codes
134(1)
9.7 Discussion and Remarks
135(3)
10 Globally Coupled QC-LDPC Codes 138(28)
10.1 Construction of CN-Based QC-GC-LDPC Codes: Method-1
138(8)
10.2 A Local/Global Two-Phase Decoding of CN-Based CPM-QC-GC-LDPC Codes
146(3)
10.3 Construction of CN-Based GC-LDPC Codes: Method-2
149(5)
10.4 CPM-Dispersion Construction of CN-Based Product QC-GC-LDPC Codes
154(8)
10.5 Discussion and Remarks
162(4)
11 SP-Construction of Nonbinary LDPC Codes 166(43)
11.1 General SP-Construction of NB LDPC Codes Using Binary Base Matrices
166(1)
11.2 SP-Construction of NB QC-LDPC Codes
167(6)
11.3 Construction of NB QC-SP-LDPC Codes Using q-ary CPM-Dispersion
173(2)
11.4 CPM-D Construction of NB CPM-QC-SP-LDPC Codes Using Binary-to-Nonbinary Replacement
175(7)
11.5 Algebraic Construction of NB QC-PTG-LDPC Codes
182(11)
11.6 Construction of NB LDPC Codes from Reed—Solomon Codes
193(4)
11.7 Construction of NB QC-SP-LDPC Codes based on RS Codes
197(8)
11.8 Discussion and Remarks
205(4)
12 Conclusion and Remarks 209(6)
Appendices 215(22)
A RC-Constrained Arrays of CPMs Constructed Based on Partial Geometries
217(8)
A.1 RC-Constrained Arrays of CPMs Constructed Based on Two-Dimensional Euclidean Geometries over Finite Fields
218(5)
A.2 RC-Constrained Arrays of CPMs Based on Partial Geometries Constructed from Prime Fields
223(2)
B An Algorithm for Searching Compatible Masking and Base Matrices for the CPM-Dispersion Construction of QC-LDPC Codes
225(3)
C Iterative Decoding Algorithm for NB LDPC Codes
228
C.1 Introduction
228(1)
C.2 Algorithm Derivation
229(6)
C.3 The NB LDPC Decoding Algorithm
235(2)
References 237(7)
Index 244
9781491937099
Preface xi
1 What Is Pig? 1(12)
Pig Latin, a Parallel Data Flow Language
1(2)
Comparing Query and Data Flow Languages
2(1)
Pig on Hadoop
3(5)
MapReduce's "Hello World"
5(1)
How Pig Differs from MapReduce
6(2)
What Is Pig Useful For?
8(2)
The Pig Philosophy
10(1)
Pig's History
10(3)
2 Installing and Running Pig 13(18)
Downloading and Installing Pig
13(3)
Downloading the Pig Package from Apache
13(1)
Installation and Setup
14(1)
Downloading Pig Artifacts from Maven
15(1)
Downloading the Source
15(1)
Downloading Pig from Distributions
15(1)
Running Pig
16(10)
Running Pig Locally on Your Machine
17(2)
Running Pig on Your Hadoop Cluster
19(2)
Running Pig in the Cloud
21(2)
Command-Line and Configuration Options
23(2)
Return Codes
25(1)
Grunt
26(2)
Entering Pig Latin Scripts in Grunt
26(1)
HDFS Commands in Grunt
27(1)
Controlling Pig from Grunt
28(1)
Running External Commands
29(1)
Others
29(2)
3 Pig's Data Model 31(14)
Types
31(5)
Scalar Types
31(3)
Complex Types
34(2)
Nulls
36(1)
Schemas
36(9)
Casts
39(6)
4 Introduction to Pig Latin 45(32)
Preliminary Matters
45(1)
Case Sensitivity
46(1)
Comments
46(1)
Input and Output
46(3)
load
46(2)
store
48(1)
dump
48(1)
Relational Operations
49(19)
foreach
49(6)
filter
55(2)
group
57(3)
order by
60(1)
distinct
61(1)
join
62(2)
limit
64(1)
sample
65(1)
parallel
65(3)
User-Defined Functions
68(9)
Registering Java UDFs
68(2)
Registering UDFs in Scripting Languages
70(1)
define and UDFs
71(1)
Calling Static Java Functions
72(1)
Calling Hive UDFs
73(4)
5 Advanced Pig Latin 77(36)
Advanced Relational Operations
77(21)
Advanced Features of foreach
77(5)
Casting a Relation to a Scalar
82(1)
Using Different Join Implementations
83(5)
cogroup
88(1)
union
89(1)
cross
90(2)
More on Nested foreach
92(1)
rank
93(2)
cube
95(3)
assert
98(1)
Integrating Pig with Executables and Native Jobs
98(4)
stream
99(2)
native
101(1)
split and Nonlinear Data Flows
102(4)
Controlling Execution
106(2)
set
106(1)
Setting the Partitioner
107(1)
Pig Latin Preprocessor
108(5)
Parameter Substitution
108(1)
Macros
109(2)
Including Other Pig Latin Scripts
111(2)
6 Developing and Testing Pig Latin Scripts 113(22)
Development Tools
113(18)
Syntax Highlighting and Checking
113(1)
describe
114(1)
explain
115(7)
illustrate
122(1)
Pig Statistics
123(2)
Job Status
125(4)
Debugging Tips
129(2)
Testing Your Scripts with PigUnit
131(4)
7 Making Pig Fly 135(16)
Writing Your Scripts to Perform Well
136(4)
Filter Early and Often
136(1)
Project Early and Often
137(1)
Set Up Your Joins Properly
138(1)
Use Multiquery When Possible
139(1)
Choose the Right Data Type
139(1)
Select the Right Level of Parallelism
139(1)
Writing Your UDFs to Perform
140(1)
Tuning Pig and Hadoop for Your Job
141(1)
Using Compression in Intermediate Results
142(1)
Data Layout Optimization
143(1)
Map-Side Aggregation
144(1)
The JAR Cache
145(1)
Processing Small Jobs Locally
146(1)
Bloom Filters
147(2)
Schema Tuple Optimization
149(1)
Dealing with Failures
150(1)
8 Embedding Pig 151(14)
Embedding Pig Latin in Scripting Languages
151(7)
Compiling
152(2)
Binding
154(2)
Running
156(1)
Utility Methods
157(1)
Using the Pig Java APIs
158(7)
PigServer
158(3)
PigRunner
161(4)
9 Writing Evaluation and Filter Functions 165(42)
Writing an Evaluation Function in Java
165(21)
Where Your UDF Will Run
166(1)
Evaluation Function Basics
166(4)
Input and Output Schemas
170(4)
Error Handling and Progress Reporting
174(2)
Constructors and Passing Data from Frontend to Backend
176(6)
Overloading UDFs
182(2)
Variable-Length Input Schema
184(1)
Memory Issues in Eval Funcs
184(1)
Compile-Time Evaluation
185(1)
Shipping JARs Automatically
185(1)
The Algebraic Interface
186(4)
The Accumulator Interface
190(2)
Writing Filter Functions
192(1)
Writing Evaluation Functions in Scripting Languages
193(14)
Jython UDFs
193(3)
JavaScript UDFs
196(2)
JRuby UDFs
198(3)
Groovy UDFs
201(4)
Streaming Python UDFs
205(1)
Comparing Scripting Language UDF Features
206(1)
10 Writing Load and Store Functions 207(24)
Load Functions
208(13)
Frontend Planning Functions
208(2)
Passing Information from the Frontend to the Backend
210(1)
Backend Data Reading
211(5)
Additional Load Function Interfaces
216(5)
Store Functions
221(7)
Store Function Frontend Planning
221(2)
Store Functions and UDFContext
223(1)
Writing Data
223(4)
Failure Cleanup
227(1)
Storing Metadata
227(1)
Shipping JARs Automatically
228(1)
Handling Bad Records
228(3)
11 Pig on Tez 231(32)
What Is Tez?
231(5)
Running Pig on Tez
236(2)
Potential Differences When Running on Tez
238(12)
UDFs
239(1)
Using PigRunner
240(2)
Testing and Debugging
242(8)
Pig on Tez Internals
250(13)
Multiple Backends in Pig
251(1)
The Tez Optimizer
252(2)
Operators and Implementation
254(6)
Automatic Parallelism
260(3)
12 Pig and Other Members of the Hadoop Community 263(12)
Pig and Hive
263(2)
HCatalog
264(1)
WebHCat
265(1)
Cascading
265(1)
Spark
266(1)
NoSQL Databases
266(5)
HBase
267(2)
Accumulo
269(1)
Cassandra
270(1)
DataFu
271(1)
Oozie
272(3)
13 Use Cases and Programming Examples 275(26)
Sparse Tuples
275(5)
k-Means
280(5)
intersect and except
285(4)
Pig at Yahoo!
289(8)
Apache Pig Use Cases at Yahoo!
290(1)
Large-Scale ETL with Apache Pig
291(1)
Features That Make Pig Attractive
291(3)
Pig on Tez
294(2)
Moving Forward
296(1)
Pig at Particle News
297(4)
Compute Arrival Rate and Conversion Rate
298(1)
Compute Sessions Triggered by a Push
299(2)
A Built-in User Defined Functions and PiggyBank 301(32)
Index 333
Juane Li is a postdoctoral researcher in the Department of Electrical and Computer Engineering at the University of California, Davis. Her current research interests are channel coding for communications and storage systems, and hardware implementation of encoders and decoders for LDPC codes. Shu Lin is an Adjunct Professor in the Department of Electrical and Computer Engineering at the University of California, Davis, and an IEEE Life Fellow. He is the co-author of Channel Codes: Classical and Modern (Cambridge, 2009) and Error Control Coding: Fundamentals and Applications (2nd edition, 2004). Khaled Abdel-Ghaffar is Professor of Electrical and Computer Engineering at the University of California, Davis. He has held research positions at the IBM Almaden Research Center, Delft University of Technology, and the University of Bergen. William E. Ryan is an IEEE Fellow and currently works for Zeta Associates, USA. He was previously a professor at New Mexico State University and the University of Arizona. He is the co-author of Channel Codes: Classical and Modern (Cambridge, 2009). Daniel J. Costello, Jr, is the Leonard Bettex Professor Emeritus of Electrical Engineering at the University of Notre Dame, and an IEEE Fellow. He is the co-author of the textbook Error Control Coding: Fundamentals and Applications (2nd edition, 2004).