林舒新书：经典与现代信道编码权威指南

5星 · 超过95%的资源需积分: 11 66 浏览量更新于2024-07-18 收藏 5.13MB PDF 举报

身份认证购VIP最低享 7 折!

30元优惠券

资源详情

资源推荐

xiv Preface

been scattered across many journal and conference papers. With this book, those

entering the field of channel coding, and those wishing to advance their knowledge,

conveniently have a single resource for learning about both classical and modern

channel codes. Further, whereas the archival literature is written for experts, this

textbook is appropriate for both the novice (earlier chapters) and the expert (later

chapters). The book begins slowly and does not presuppose prior knowledge in the

field of channel coding. It then extends to frontiers of the field, as is evident from

the table of contents.

The topics selected for this book, of course, reflect the experiences and interests

of the authors, but they were also selected for their importance in the study of

channel codes – not to mention the fact that additional chapters would make the

book physically unwieldy. Thus, the emphasis of this book is on codes for binary-

input channels, including the binary-input additive white-Gaussian-noise channel,

the binary symmetric channel, and the binary erasure channel. One notable area

of omission is coding for wireless channels, such as MIMO channels. However, this

book is useful for students and researchers in that area as well because many of

the techniques applied to the additive white-Gaussian-noise channel, our main

emphasis, can be extended to wireless channels. Another notable omission is soft-

decision decoding of Reed–Solomon codes. While extremely important, this topic

is not as mature as those in this book.

Several different course outlines are possible with this book. The most obvious

for a first graduate course on channel codes includes selected topics from Chap-

ters 1, 2, 3, 4, 5, and 7. Such a course introduces the student to capacity limits for

several common channels (Chapter 1). It then provides the student with an intro-

duction to just enough algebra (Chapter 2) to understand BCH and Reed–Solomon

codes and their decoders (Chapter 3). Next, this course introduces the student to

convolutional codes and their decoders (Chapter 4). This course next provides the

student with an introduction to LDPC codes and iterative decoding (Chapter 5).

Finally, with the knowledge gained from Chapters 4 and 5 in place, the student

is ready to tackle turbo codes and turbo decoding (Chapter 7). The material con-

tained in Chapters 1, 2, 3, 4, 5, and 7 is too much for a single-semester course and

the instructor will have to select a preferred subset of that material.

For a more advanced course centered on LDPC code design, the instructor could

select topics from Chapters 10, 11, 12, 13, and 14. This course would first intro-

duce the student to LDPC code design using Euclidean geometries and projective

geometries (Chapter 10). Then the student would learn about LDPC code design

using finite fields (Chapter 11) and combinatorics and graphs (Chapter 12). Next,

the student would apply some of the techniques from these earlier chapters to

design codes for the binary erasure channel (Chapter 13). Lastly, the student

would learn design techniques for nonbinary LDPC codes (Chapter 14).

As a final example of a course outline, a course could be centered on computer-

based design of LDPC codes. Such a course would include Chapters 5, 6, 8,

and 9. This course would be for those who have had a course on classical channel

Preface

codes, but who are interested in LDPC codes. The course would begin with an

introduction to LDPC codes and various LDPC decoders (Chapter 5). Then,

the student would learn about various computer-based code-design approaches,

including Gallager codes, MacKay codes, codes based on protographs, and codes

based on accumulators (Chapter 6). Next, the student would learn about assessing

the performance of LDPC code ensembles from a weight-distribution perspective

(Chapter 8). Lastly, the student would learn about assessing the performance of

(long) LDPC codes from a decoding-threshold perspective via the use of density

evolution and EXIT charts (Chapter 9).

All of the chapters contain a good number of problems. The problems are of

various types, including those that require routine calculations or derivations,

those that require computer solution or computer simulation, and those that might

be characterized as a semester project. The authors have selected the problems

to strengthen the student’s knowledge of the material in each chapter (e.g., by

requiring a computer simulation of a decoder) and to extend that knowledge (e.g.,

by requiring the proof of a result not contained in the chapter).

We wish to thank, first of all, Professor Ian Blake, who read an early version

of the entire manuscript and provided many important suggestions that led to a

much improved book.

We also wish to thank the many graduate students who have been a tremendous

help in the preparation of this book. They have helped with typesetting, computer

simulations, proofreading, and figures, and many of their research results can be

found in this book. The students (former and current) who have contributed to

W. Ryan’s portion of the book are Dr. Yang Han, Dr. Yifei Zhang, Dr. Michael

(Sizhen) Yang, Dr. Yan Li, Dr. Gianluigi Liva, Dr. Fei Peng, Shadi Abu-Surra,

Kristin Jagiello (who proofread eight chapters), and Matt Viens. Gratitude is also

due to Li Zhang (S. Lin’s student) who provided valuable feedback on Chapters 6

and 9. Finally, W. Ryan acknowledges Sara Sandberg of Lule˚a Institute of Tech-

nology for helpful feedback on an early version of Chapter 5. The students who

have contributed to S. Lin’s portion of the book are Dr. Bo Zhou, Qin Huang,

Dr. Ying Y. Tai, Dr. Lan Lan, Dr. Lingqi Zeng, Jingyu Kang, and Li Zhang; Dr.

Bo Zhou and Qin Huang deserve special appreciation for typing S. Lin’s chapters

and overseeing the preparation of the final version of his chapters.

We thank Professor Dan Costello, who sent us reference material for the con-

volutional LDPC code section in Chapter 15, Dr. Marc Fossorier, who provided

comments on Chapter 14, and Professor Ali Ghrayeb, who provided comments on

Chapter 7.

We are grateful to the National Science Foundation, the National Aeronautics

and Space Administration, and the Information Storage Industry Consortium for

their many years of funding support in the area of channel coding. Without their

support, many of the results in this book would not have been possible. We also

thank the University of Arizona and the University of California, Davis for their

support in the writing of this book.

Coding and Capacity

1.1 Digital Data Communication and Storage

Digital communication systems are ubiquitous in our daily lives. The most obvious

examples include cell phones, digital television via satellite or cable, digital radio,

wireless internet connections via Wi-Fi and WiMax, and wired internet connec-

tion via cable modem. Additional examples include digital data-storage devices,

including magnetic (“hard”) disk drives, magnetic tape drives, optical disk drives

(e.g., CD, DVD, blu-ray), and flash drives. In the case of data-storage, informa-

tion is communicated from one point in time to another rather than one point in

space to another. Each of these examples, while widely different in implementa-

tion details, generally fits into a common digital communication framework first

established by C. Shannon in his 1948 seminal paper, A Mathematical Theory

of Communication [1]. This framework is depicted in Figure 1.1, whose various

components are described as follows.

Source and user (or sink ). The information source may be originally in analog

form (e.g., speech or music) and then later digitized, or it may be originally in

digital form (e.g., computer files). We generally think of its output as a sequence

of bits, which follow a probabilistic model. The user of the information may be

a person, a computer, or some other electronic device.

Source encoder and source decoder. The encoder is a processor that converts

the information source bit sequence into an alternative bit sequence with a

more efficient representation of the information, i.e., with fewer bits. Hence, this

operation is often called compression. Depending on the source, the compression

can be lossless (e.g., for computer data files) or lossy (e.g., for video, still images,

or music, where the loss can be made to be imperceptible or acceptable). The

source decoder is the encoder’s counterpart which recovers the source sequence

exactly, in the case of lossless compression, or approximately, in the case of lossy

compression, from the encoder’s output sequence.

Channel encoder and channel decoder. The role of the channel encoder is to

protect the bits to be transmitted over a channel subject to noise, distortion,

and interference. It does so by converting its input into an alternate sequence

possessing redundancy, whose role is to provide immunity from the various

2 Coding and Capacity

Source

encoder

Channel

encoder

Modulator

Source

decoder

Channel

decoder

DemodulatorUser

Channel

Figure 1.1 Basic digital communication- (or storage-) system block diagram due to Shannon.

channel impairments. The ratio of the number of bits that enter the channel

encoder to the number that depart from it is called the code rate, denoted by

R, with 0 <R<1. For example, if a 1000-bit codeword is assigned to each

500-bit information word, R =1/2, and there are 500 redundant bits in each

codeword. The function of the channel decoder is to recover from the channel

output the input to the channel encoder (i.e., the compressed sequence) in spite

of the presence of noise, distortion, and interference in the received word.

Modulator and demodulator. The modulator converts the channel-encoder out-

put bit stream into a form that is appropriate for the channel. For example,

for a wireless communication channel, the bit stream must be represented by

a high-frequency signal to facilitate transmission with an antenna of reason-

able size. Another example is a so-called modulation code used in data storage.

The modulation encoder output might be a sequence that satisfies a certain

runlength constraint (runs of like symbols, for example) or a certain spectral

constraint (the output contains a null at DC, for example). The demodulator is

the modulator’s counterpart which recovers the modulator input sequence from

the modulator output sequence.

Channel. The channel is the physical medium through which the modulator

output is conveyed, or by which it is stored. Our experience teaches us that

the channel adds noise and often interference from other signals, on top of the

signal distortion that is ever-present, albeit sometimes to a minor degree. For

our purposes, the channel is modeled as a probabilistic device, and examples will

be presented below. Physically, the channel can included antennas, amplifiers,

and filters, both at the transmitter and at the receiver at the ends of the system.

For a hard-disk drive, the channel would include the write head, the magnetic

medium, the read head, the read amplifier and filter, and so on.

Following Shannon’s model, Figure 1.1 does not include such blocks as encryp-

tion/decryption, symbol-timing recovery, and scrambling. The first of these is

optional and the other two are assumed to be ideal and accounted for in the

probabilistic channel models. On the basis of such a model, Shannon showed that

a channel can be characterized by a parameter, C, called the channel capacity,

which is a measure of how much information the channel can convey, much like

剩余709页未读，继续阅读

清江渔人

粉丝: 3
资源: 3

林舒新书：经典与现代信道编码权威指南

[2009] Channel Codes Classical and Modern

差错控制编码 Error Control Coding 英文版 第二版 2nd 有完整书签 林舒 lin shu part1

差错控制编码(原书第2版)_[林舒.著][中文].part1

channel codes: classical and modern

Error Separation and Correction codes

Algebraic codes for data transmission

write me a ultrasound channel data generator

Low-density parity-check codes constructed from protographs

| unique(self) | Return the ``Categorical`` which ``categories`` and ``codes`` are | unique.

for i in codes: f[i]=codes

@RequestParam("codes") List<String> codes

TypeError: Must pass both levels and codes

如果需要读取指定的关键字呢

smd-codes datebook

最新资源

差错控制编码 Error Control Coding 英文版第二版 2nd 有完整书签林舒 lin shu part1