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E-raamat: Fundamentals of Convolutional Coding 2e 2nd Edition [Wiley Online]

(Lund University, Sweden),
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Fundamentals of Convolutional Coding 2e 2nd EditionSuurem pilt
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Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119098799
Convolutional codes, among the main error control codes, are routinely used in applications for mobile telephony, satellite communications, and voice-band modems. Written by two leading authorities in coding and information theory, this book brings you a clear and comprehensive discussion of the basic principles underlying convolutional coding. FUNDAMENTALS OF CONVOLUTIONAL CODING is unmatched in the field for its accessible analysis of the structural properties of convolutional encoders.

The new edition, updated and revised throughout, includes two new chapters, "Low-Density Parity-Check Convolutional Codes", "Iterative Decoding."

Other essentials covered in FUNDAMENTALS OF CONVOLUTIONAL CODING include:

  • Distance properties of convolutional codes
  • Viterbi, list, sequential, and iterative decoding
  • Modulation codes
  • Tables of good convolutional encoders
  • An extensive set of homework problems.

The authors draw on their own research and more than twenty years of teaching experience to present the fundamentals needed to understand the types of codes used in a variety of applications today. This book can be used as a textbook for graduate-level electrical engineering students. It will be of key interest to researchers and engineers of wireless and mobile communications, satellite communication, and data communication."

Preface xi
Acknowledgement xiv
1 Introduction 1(48)
1.1 Why error control?
1(7)
1.2 Block codes-a primer
8(13)
1.3 Codes on graphs
21(7)
1.4 A first encounter with convolutional codes
28(7)
1.5 Block codes versus convolutional codes
35(1)
1.6 Capacity limits and potential coding gain revisited
36(3)
1.7 Comments
39(2)
Problems
41(8)
2 Convolutional encoders-Structural properties 49(112)
2.1 Convolutional codes and their encoders
49(9)
2.2 The Smith form of polynomial convolutional generator matrices
58(9)
2.3 Encoder inverses
67(9)
2.4 Encoder and code equivalences
76(3)
2.5 Basic encoding matrices
79(3)
2.6 Minimal-basic encoding matrices
82(8)
2.7 Minimal encoding matrices and minimal encoders
90(19)
2.8 Canonical encoding matrices
109(18)
2.9 Minimality via the invariant-factor theorem
127(4)
2.10 Syndrome formers and dual encoders
131(8)
2.11 Systematic convolutional encoders
139(11)
2.12 Some properties of generator matrices-an overview
150(1)
2.13 Comments
150(2)
Problems
152(9)
3 Distance properties of convolutional codes 161(64)
3.1 Distance measures-a first encounter
161(10)
3.2 Active distances
171(8)
3.3 Properties of convolutional codes via the active distances
179(2)
3.4 Lower bound on the distance profile
181(5)
3.5 Upper bounds on the free distance
186(5)
3.6 Time-varying convolutional codes
191(4)
3.7 Lower bound on the free distance
195(5)
3.8 Lower bounds on the active distances
200(7)
3.9 Distances of cascaded concatenated codes
207(6)
3.10 Path enumerators
213(7)
3.11 Comments
220(1)
Problems
221(4)
4 Decoding of convolutional codes 225(108)
4.1 The Viterbi algorithm revisited
226(9)
4.2 Error bounds for time-invariant convolutional codes
235(15)
4.3 Tighter error bounds for time-invariant convolutional codes
250(5)
4.4 Exact bit error probability for Viterbi decoding
255(16)
4.5 The BCJR algorithm for APP decoding
271(12)
4.6 The one-way algorithm for APP decoding
283(5)
4.7 A simple upper bound on the bit error probability for extremely noisy channels
288(5)
4.8 Tailbiting trellises
293(9)
4.9 Decoding of tailbiting codes
302(6)
4.10 BEAST decoding of tailbiting codes
308(15)
4.11 Comments
323(1)
Problems
324(9)
5 Random ensemble bounds for decoding error probability 333(54)
5.1 Upper bounds on the output error burst lengths
333(12)
5.2 Bounds for periodically time-varying convolutional codes
345(10)
5.3 Lower error probability bounds for convolutional codes
355(8)
5.4 General bounds for time-varying convolutional codes
363(12)
5.5 Bounds for finite back-search limits
375(4)
5.6 Quantization of channel outputs
379(5)
5.7 Comments
384(1)
Problems
384(3)
6 List decoding 387(38)
6.1 List decoding algorithms
388(3)
6.2 List decoding-performance
391(6)
6.3 The list minimum weight
397(10)
6.4 Upper bounds on the probability of correct path loss
407(9)
6.5 Lower bound on the probability of correct path loss
416(2)
6.6 Correct path loss for time-invariant convolutional codes
418(4)
6.7 Comments
422(1)
Problems
423(2)
7 Sequential decoding 425(60)
7.1 The Fano metric
426(5)
7.2 The stack algorithm
431(2)
7.3 The Fano algorithm
433(3)
7.4 The Creeper algorithm
436(12)
7.5 Simulations
448(2)
7.6 Computational analysis of the stack algorithm
450(10)
7.7 Error probability analysis of the stack algorithm
460(11)
7.8 Analysis of the Fano algorithm
471(6)
7.9 Analysis of Creeper
477(3)
7.10 Comments
480(1)
Problems
481(4)
8 Low-density parity-check codes 485(82)
8.1 LDPC block codes
486(10)
8.2 LDPC convolutional codes
496(12)
8.3 Block and convolutional permutors
508(9)
8.4 Lower bounds on distances of LDPC codes
517(12)
8.5 Iterative decoding of LDPC codes
529(9)
8.6 Iterative limits and thresholds
538(15)
8.7 Braided block codes
553(9)
8.8 Comments
562(1)
Problems
562(5)
9 Turbo coding 567(26)
9.1 Parallel concatenation of two convolutional codes
567(3)
9.2 Distance bounds of turbo codes
570(3)
9.3 Parallel concatenation of three and more convolution codes
573(9)
9.4 Iterative decoding of turbo codes
582(4)
9.5 Braided convolutional codes
586(5)
9.6 Comments
591(1)
Problems
591(2)
10 Convolutional codes with good distance properties 593(34)
10.1 Computing the Viterbi spectrum using FAST
594(4)
10.2 The magnificient BEAST
598(6)
10.3 Some classes of rate R =- 1/2 convolutional codes
604(4)
10.4 Low rate convolutional codes
608(13)
10.5 High rate convolutional codes
621(1)
10.6 Tailbiting trellis encoders
622(1)
10.7 Comments
622(5)
Appendix A: Minimal encoders 627(8)
Appendix B: Wald's identity 635(12)
References 647(12)
Index 659
Rolf Johannesson is Professor Emeritus of Information Theory at Lund University, Sweden, and a Fellow of the IEEE. He was awarded the honor of Professor, honoris causa, from the Institute for Information Transmission Problems, Russian Academy of Sciences, and elected member of the Royal Swedish Academy of Engineering Sciences. Dr. Johannesson's research interests include information theory, coding theory, and cryptography.

Kamil Sh. Zigangirov is Professor Emeritus of Telecommunication Theory at Lund University, Sweden, and a Fellow of the IEEE. He is widely published in the areas of information theory, coding theory, mathematical statistics, and detection theory. Dr. Zigangirov is the inventor of the stack algorithm for sequential decoding and the co-inventor of the LDPC convolutional codes.
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