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DESIGN AND ANALYSIS OF LDPC CONVOLUTIONAL CODES
A Dissertation
Submitted to the Graduate School
of the University of Notre Dame
in Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
by
Arvind Sridharan, M. S.
Daniel J. Costello, Jr., Director
Graduate Program in Electrical Engineering
Notre Dame, Indiana
February 2005
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c Copyright byArvind Sridharan
2005
All Rights Reserved
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DESIGN AND ANALYSIS OF LDPC CONVOLUTIONAL CODES
Abstract
by
Arvind Sridharan
Low-density parity-check (LDPC) block codes invented by Gallager in 1962
achieve exceptional error performance on a wide variety of communication chan-
nels. LDPC convolutional codes are the convolutional counterparts of LDPC block
codes. This dissertation describes techniques for the design of LDPC convolutional
codes, and analyzes their distance properties and iterative decoding convergence
thresholds.
The construction of time invariant LDPC convolutional codes by unwrapping
the Tanner graph of algebraically constructed quasi-cyclic LDPC codes is de-
scribed. The convolutional codes outperform the quasi-cyclic codes from which
they are derived. The design of parity-check matrices for time invariant LDPC
convolutional codes by the polynomial extension of a base matrix is proposed. An
upper bound on free distance, proving that time invariant LDPC codes are not
asymptotically good, is obtained.
The Tanner graph is used to describe a pipelined message passing based itera-
tive decoder for LDPC convolutional codes that outputs decoding results continu-
ously. The performance of LDPC block and convolutional codes are compared for
fixed decoding parameters like computational complexity, processor complexity,
and decoding delay. In each case, the LDPC convolutional code performance is
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Arvind Sridharanat least as good as that of LDPC block codes. An analog circuit to implement
pipelined decoding of LDPC convolutional codes is proposed.
The distance properties of a permutation matrix based (time varying) ensemble
of (J, K) regular LDPC convolutional codes is investigated. It is proved that these
codes (for J > 2) have free distance increasing linearly with constraint length, i.e.,
they are asymptotically good. Further, the asymptotic free distance to constraint
length ratio for the convolutional codes is several times larger than the minimum
distance to block length ratio for corresponding LDPC block codes.
Iterative decoding of terminated LDPC convolutional codes, based on the en-
semble mentioned above, is analyzed, assuming transmission over a binary erasure
channel. The structured irregularity of the codes leads to significantly better con-
vergence thresholds compared to corresponding LDPC block codes. At the calcu-
lated thresholds, both the bit and block error probability can be made arbitrarily
small. The results obtained suggest that the thresholds approach capacity with
increasing J.
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To all my teachers
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CONTENTS
FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . x
CHAPTER 1: INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . 11.1 Digital communication systems . . . . . . . . . . . . . . . . . . . 11.2 Channel models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Channel codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.1 Block codes . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3.2 Convolutional codes . . . . . . . . . . . . . . . . . . . . . 10
1.4 Channel decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.4.1 Optimal decoding . . . . . . . . . . . . . . . . . . . . . . . 131.4.2 Iterative decoding . . . . . . . . . . . . . . . . . . . . . . . 14
1.5 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . 15
CHAPTER 2: BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . 172.1 LDPC block codes . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2 Iterative decoding techniques . . . . . . . . . . . . . . . . . . . . 202.3 LDPC convolutional codes . . . . . . . . . . . . . . . . . . . . . . 24
CHAPTER 3: LDPC CONVOLUTIONAL CODES CONSTRUCTED FROMQUASI-CYCLIC CODES . . . . . . . . . . . . . . . . . . . . . . . . . 303.1 Code construction . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1.1 Construction of quasi-cyclic LDPC block codes . . . . . . 313.1.2 Construction of LDPC convolutional codes . . . . . . . . . 36
3.2 Properties of constructed codes . . . . . . . . . . . . . . . . . . . 423.2.1 Relation between the block and convolutional Tanner graphs 42
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3.2.2 Quasi-cyclic block codes viewed as tail-biting convolutionalcodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.3 Girth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.2.4 Minimum distance . . . . . . . . . . . . . . . . . . . . . . 463.2.5 Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 Performance results . . . . . . . . . . . . . . . . . . . . . . . . . . 49
CHAPTER 4: A CONSTRUCTION FOR TIME INVARIANT LDPC CON-VOLUTIONAL CODES . . . . . . . . . . . . . . . . . . . . . . . . . . 554.1 Code construction technique . . . . . . . . . . . . . . . . . . . . . 564.2 Choosing the base matrix . . . . . . . . . . . . . . . . . . . . . . 60
4.2.1 Girth properties . . . . . . . . . . . . . . . . . . . . . . . . 614.2.2 Optimizing thresholds . . . . . . . . . . . . . . . . . . . . 62
4.3 Distance bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
CHAPTER 5: IMPLEMENTATION OF LDPC CONVOLUTIONAL CODE
DECODERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675.1 Decoder description . . . . . . . . . . . . . . . . . . . . . . . . . . 685.2 Comparison of message passing decoding of block and convolutional
codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735.3 Analog decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.3.1 Analog decoding of LDPC convolutional codes . . . . . . . 79
CHAPTER 6: DISTANCE BOUNDS FOR LDPC CONVOLUTIONALCODES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 876.1 An LDPC convolutional code ensemble . . . . . . . . . . . . . . . 88
6.2 A lower bound on row distance . . . . . . . . . . . . . . . . . . . 926.2.1 Row distance . . . . . . . . . . . . . . . . . . . . . . . . . 936.2.2 Probability for a single block of constraints . . . . . . . . . 956.2.3 Lower bound on row distance . . . . . . . . . . . . . . . . 102
6.3 A lower bound on free distance . . . . . . . . . . . . . . . . . . . 1076.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . 111
CHAPTER 7: CONVERGENCE ANALYSIS ON THE ERASURE CHAN-NEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177.1 Tanner graph description . . . . . . . . . . . . . . . . . . . . . . . 1187.2 Termination of LDPC convolutional codes . . . . . . . . . . . . . 119
7.3 Decoding analysis on the erasure channel . . . . . . . . . . . . . . 1347.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
CHAPTER 8: CONCLUDING REMARKS . . . . . . . . . . . . . . . . . 147
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APPENDIX A: MAXIMUM OF THE FUNCTION U([1,L]) . . . . . . . . 150BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
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FIGURES
1.1 Block diagram of a digital communication system. . . . . . . . . . 2
1.2 The binary erasure channel . . . . . . . . . . . . . . . . . . . . . . 5
1.3 The binary symmetric channel . . . . . . . . . . . . . . . . . . . . 5
1.4 Illustration of the additive white Gaussian noise channel model. . 6
1.5 A rate 1/2 convolutional encoder with memory 2. . . . . . . . . . 10
2.1 The Tanner graph of the parity-check matrix of Example 2.1.1. TheTanner graph consists of 20 symbol nodes, each with J = 3 edgesand 15 constraint nodes, each with K = 4 edges. . . . . . . . . . 19
2.2 Illustration of the Jimenez-Zigangirov method to construct the syn-drome former of a b = 1, c = 2 LDPC convolutional (4, 3, 6) code . 26
2.3 The Tanner graph for the convolutional code of Example 2.3.2. . . 28
3.1 Tanner graph for a [155,64,20] QC code. . . . . . . . . . . . . . . 35
3.2 Tanner graph for a [21,8,6] QC code. . . . . . . . . . . . . . . . . 36
3.3 Tanner graph for the rate 1/3 convolutional code of Example 5. . 42
3.4 A 12-cycle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5 A systematic syndrome former based encodes for the R = 1/3 codeof Example 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.6 The performance of convolutional LDPC codes versus the corre-sponding QC block LDPC codes for (3,5) connectivity. . . . . . . 50
3.7 The performance of convolutional LDPC codes versus the corre-sponding QC block LDPC codes for (5,7) connectivity. . . . . . . 52
3.8 The performance of convolutional LDPC codes versus the corre-sponding QC block LDPC codes for (3,17) connectivity. . . . . . . 53
3.9 Performance of regular (3, 5) QC block codes with increasing circu-lant size m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
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4.1 Performance of the codes in Examples 4.1.1-4.1.3 on the AWGNchannel. The performance of a QC code with N = 1600 obtainedfrom the convolutional code of Example 4.1.3 is also shown. . . . 60
4.2 Existence of six cycle with polynomials of weight 3 or more. . . . 61
5.1 The sliding window decoder for I iterations of the code of Example 5. 705.2 Continuous decoding of the code of Example 5 with I iterations. . 71
5.3 Performance comparison of LDPC block and convolutional codes onthe AWGN channel for the same processor complexity and decodingdelays. The block code is decoded with a very fast decoder. . . . . 76
5.4 Serial decoding of LDPC block codes using I decoders. . . . . . . 77
5.5 Transistor circuits for implementing the symbol (SYM) and con-straint (CHK) node updates. . . . . . . . . . . . . . . . . . . . . . 78
5.6 The rotating ring decoder . . . . . . . . . . . . . . . . . . . . . . 80
5.7 Bit error rate curves on the AWGN channel with rotating ring de-coders (Type c)) of different window sizes. The ML performance isalso shown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.8 Bit error rate curves for different decoder types with W=9. Typea) is the usual sliding window decoder. Type b) is a rotating ringdecoder with channel values reloaded at each time. Type c) is astandard rotating window decoder (no reloading) . . . . . . . . . 83
5.9 Decoding results for the time discrete decoder (left two columns)and the time continuous decoder (right two columns) for one re-ceived block of length 55 bits. Each row represents a different test
case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.10 Probability distributions of output LLRs for analog and digital de-coders. L(z) is the decoder output and p(L(z)) is the probabilitydistribution function of the random variable L(z). . . . . . . . . . 86
6.1 syndrome former for a code in CP(3, 6, M). . . . . . . . . . . . . . . 89
6.2 Permuting rows of a syndrome former HT in CP(3, 6, M). . . . . . 916.3 Illustration of length L = 3 segment ofHT[1,L]. . . . . . . . . . . . 94
6.4 Set of parameters3 and 3 for calculating the function F3([1,3],[1,3])1036.5 Lower bound on row distance to constraint length ratio d(L)6M as afunction of L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.6 Maximizing weight vector [1,L] for different L. . . . . . . . . . . . 113
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7.1 Tanner graph connections of the 2M symbol nodes at time t. . . . 118
7.2 Tanner graph connections of the M parity-check equations at time t.119
7.3 The matrix HT[L+1,L+3]. . . . . . . . . . . . . . . . . . . . . . . . . 123
7.4 The matrices resulting from row operations on A. . . . . . . . . . 125
7.5 The matrices C3 and C2. . . . . . . . . . . . . . . . . . . . . . . 1307.6 Tanner graph of a terminated convolutional code obtained from
C(M, 2M, 3). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1337.7 Illustration of the messages (a) to a symbol node and (b) to a
constraint node for the case J = 3. . . . . . . . . . . . . . . . . . 138
7.8 The first level of computation trees for t = 1, 2, 3 with J = 3. . . . 139
7.9 Illustration of the tunneling effect and the breakout value . . . . . 143
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TABLES
3.1 EXAMPLES OF CODES CONSTRUCTED FROM (PRIME) CIR-CULANT SIZES. . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1 BEST THRESHOLDS OBTAINED FOR BASE MATRICES OFDIFFERENT SIZES . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.1 DISTANCE BOUND FOR LDPC BLOCK AND CONVOLUTIONAL
CODES FOR DIFFERENT (J, K). . . . . . . . . . . . . . . . . . 115
7.1 THRESHOLDS FOR THE ENSEMBLE CP(3, 6, M) WITH DIF-FERENT L AND FOR THE CORRESPONDING IRREGULARBLOCK CODES. . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
7.2 THRESHOLDS FOR THE ENSEMBLES CP(J, 2J, M) WITH DIF-FERENT J. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
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ACKNOWLEDGMENTS
This dissertation would not have been possible without the help of numerous
people over the past five years. Although, it is only possible to thank a few here,
I would like to express my gratitude to all of them.
I would like to thank Dr. Costello for being a wonderful adviser. He provided
constant support, encouragement, made himself readily available, and always had
a patient ear, even for all the nonsense I had to say. Many thanks for the wonderful
six months in Germany!
Dr. Tanner gave a series of interesting talks in the Fall of 2000. I would like
to thank him for these talks and other stimulating discussions which has resulted
in a chapter of this dissertation.
Dr. Kamil Zigangirovs help in shaping my research cannot be over empha-sized. Kamils down to earth personality and enthusiasm meant that working with
him has not only been a great opportunity and learning experience but also great
fun. My heartfelt thanks to him also for the the wonderful dinners he cooked and
for the demonstrations of the art of drinking vodka, in both Russian and Swedish
styles.
Dr. Michael Lentmaier, during his time at Notre Dame, taught me the value
of being systematic. His clear insight and attitude to research was a wonderful
example. Very often he dropped everything he was doing to discuss my research
problems. These discussions have been immensely useful.
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I would like to thank Dr. Fuja and Dr. Collins for serving on my defense
committee, and Dr. Peter Massey, Andrew Schaefer, and Dr. Dmitry Trukhachev
for the opportunity to discuss and work together.
My friends and colleagues at Notre Dame; Adrish, Ajay, Chandra, Junying,Ching, Ravi, Rajkumar, Tony, Ajit, Hongzhi, Shirish, Vasu, Sridhar, Ali, Xiaowei,
Guanjun, Kameshwar, Shiva, Jagadish, to name a few made Notre Dame a pleas-
ant place to work at. Special thanks to Ali for meeting every single inane problem
I had about using the computer with a smile and solving it. I would also like to
thank Sameer for providing the LATEXclass file used to format the dissertation.
I cannot thank the EE department staff enough for all their help. I would like
to specially thank Pat for always doing more than was needed.
The music concerts, cinema, theater, wonderful library, and sports facilities at
Notre Dame meant I always had more to do than time permitted. The opportunity
to see several plays of Shakespeare enacted on stage by Actors from the London
Stage will remain an unforgettable part of my time at Notre Dame.
A significant reason for the wonderful time I had at Notre Dame was Deepak.
During his time at Notre Dame, Deepak was my friend, philosopher, guide, punch-
ing bag, and more. I have had countless discussions with him on virtually every-
thing under the sun. I consider myself fortunate to have spent time with him.
My parents and Arathi made home a wonderful place to be. They have always
provided me with unconditional support, love, and encouragement. Anthony,
Clement, and Dilip have taught me much and continue to do so. I am much the
better for knowing them.
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CHAPTER 1
INTRODUCTION
Today the use of digital cell phones, the internet, and CD and DVD players is
ubiquitous. In all of these cases, digitally represented data is either transmitted
from one place to another or retrieved from a storage device when needed. For
the proper functioning of these systems the transmitted or retrieved data must
be sufficiently error free. This can be accomplished efficiently by using channel
coding techniques. One such channel coding technique for achieving reliable data
transmission or retrieval is to use low-density parity-check (LDPC) convolutional
codes. The design of LDPC convolutional codes and an analysis of their properties
are the topic of this dissertation.
The next section describes a model of a general digital communication system.
In Section 1.2, the discrete time model of the communication channels considered
throughout this dissertation is described. The next two sections describe the basic
concepts of channel coding and decoding. In Section 1.5, an outline of the rest of
the thesis is presented.
1.1 Digital communication systems
Most generally, a communication system aims at transmitting information
from a source to a destination across a channel. The nature of the source and
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Channel
Channel
decoder
encoder
Demodulator
Modulator
Discrete ChannelBinary Information Source
Binary Information Sink
Source
Source
ChannelPhysical
Destination
Sourceencoder
decoder
u v
u r
Figure 1.1. Block diagram of a digital communication system.
destination, e.g., computers exchanging messages or a conversation between two
humans, is not of importance. Similarly, what the information exchanged repre-
sents, e.g., music or conversation, is not essential. The information exchange can
take place across two different locations, as in the case of telephone conversations,
or across both different times and locations as in the case of storage and retrieval
systems like CDs.
Claude Shannon, in his famous paper [1], pioneered the area of information
theory. This paper quantified the notion of information and has been the basis of
the study and design of communication systems since. One of Shannons results
is that it is possible to separate the processing of the source output before trans-
mission into source encoding and channel encoding without sacrificing optimality.
The general model of a digital communication system based on this separation
principle is shown in Fig. 1.1.
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Typically, the data generated from most sources has redundancy, the trans-
mission of which can be avoided. For example, in a phone conversation there are
often periods of silence in the conversation which need not be transmitted. The
function of the source encoder is two fold, one, convert the information from thesource into a binary sequence u, the information sequence, and two, remove all
redundancy in the source information in obtaining u.
Probably the most remarkable result obtained by Shannon in [1] is the noisy
channel coding theorem. This theorem states that, for a broad class of channels,
reliable transmission is possible as long as the transmission rate is less than the
channel capacity. This is achieved by introducing redundancy, by means of a
channel code, suited for overcoming the debilitating effects of the channel on the
data to be transmitted. This result led to the subject of coding theory, which
investigates the use of efficient and practical coding schemes to achieve reliable
communication at information rates close to the capacity of the channel. The
function of the channel code is to add redundancy in a controlled fashion to the
information sequence u and generate the code sequence v to facilitate reliable
transmission to the destination.
The modulator converts symbols of the code sequence v into a form suited
for transmission across the physical channel. The physical channel is the medium
across which data is transmitted. It can be, for example, a simple wire, a storage
medium like a CD, or the wireless channel from a base station to a cell phone user.
The channel, while allowing the transfer of data, corrupts the transmitted signal
from the modulator in some fashion. As already noted, Shannon proved that as
long as the transmission rate is less than the channel capacity these effects can be
overcome.
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The blocks after the channel undo the operations performed during data trans-
mission. The demodulator generates a discrete sequence r, the received sequence,
from the output of the channel. The channel decoder uses r to generate an esti-
mate u of the information sequence u. Ideally, u is the same as u. The sourcedecoder uses the estimated information sequence u and regenerates the sourcedata in the form required by the destination.
In the scope of this work, we are concerned with channel encoding and de-
coding. We therefore group the blocks in Fig. 1.1 as shown so that we obtain a
binary information source, a discrete channel, and a binary information sink. The
information sequence1 u is transmitted from the binary source across a discrete
channel to the binary sink. The discrete channel corrupts the transmitted code
sequence and this can be overcome by appropriately designed coding schemes.
1.2 Channel models
In this section we describe three different models for the discrete channel,
the binary erasure channel (BEC), the binary symmetric channel (BSC), and the
additive white Gaussian noise (AWGN) channel.
The binary erasure channel is shown in Fig 1.2. In this case, there are three
possible channel outputs, the transmitted symbols 0 and 1, or an erasure . The
BEC does not cause any errors, a symbol is either received correctly or as an
erasure. The probability of a symbol (0 or 1) being received as an erasure, the
erasure probability of the channel, is p. The BEC has capacity 1 p bits per
channel use.1For an ideal source encoder, the bits of the information sequence are independent and equally
likely and we assume that this is the case throughout the dissertation.
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p
p
1 p
1 p 00
11
e
Figure 1.2. The binary erasure channel
p
p
1 p
1 p 00
11
Figure 1.3. The binary symmetric channel
The BSC is shown in Fig 1.3. In this case a 0 can be erroneously received as
a 1 or vice versa with crossover probabilityp. The BSC has capacity 1 h(p) bitsper channel use where the binary entropy function h(p) is defined as
h(p) = p log2p (1 p)log2(1 p) (1.1)
The third model we consider, the AWGN channel, has binary inputs as earlier
but the outputs are real numbers. The model for the AWGN channel is shown in
Fig. 1.4. The modulator maps the bits ofv into antipodal waveforms with energy
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Modulator +vt
{0, 1
}
nt N(0, N0/2)
rt
R
Es
Figure 1.4. Illustration of the additive white Gaussian noise channelmodel.
Es. The waveforms are represented by their amplitudes
Es in Fig. 1.4. The
channel adds noise with one-sided power spectral density N0 to the transmitted
waveforms. The optimal demodulator generates at each time t the real number
rt = (1 2vt)
Es + nt, vt {0, 1}, (1.2)
where nt is a Gaussian random variable with variance 2 = N0/2. The received
value rt is therefore a random variable with conditional probability density func-
tion
p(rt|vt) = 1N0
e(rt(12vt )Es)2/N0 . (1.3)
The AWGN channel model is a good model for channels where noise in the chan-
nel is the result of a superposition of a large number of independent identically
distributed noise sources. The capacity of the discrete AWGN channel considered
here must be calculated numerically. In this dissertation we will deal mainly with
the AWGN channel except for the penultimate chapter where we focus on the
erasure channel.
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1.3 Channel codes
Channel codes introduce redundancy into the information sequence so that it
is possible to correct erroneous symbols altered by the channel and thus ensure
reliable transmission. For this reason, channel codes are often referred to as error
correcting codes. In this section, a brief introduction to error correcting codes is
presented. We review the two main classes of codes, block codes and convolutional
codes, and associated concepts. A more thorough treatment of block codes can
be found in several textbooks [2][3][4][5][6]. Convolutional codes are described in
detail in [7][6].
1.3.1 Block codes
Consider data transmission over a BSC. A simple way to combat errors in
this case is to repeat each information bit N times, a length N repetition code.
The decoder uses a majority vote to determine the information bit. The decoding
decision is correct as long as the number of errors is less than N/2. Thus by
increasing N, arbitrary reliability can be achieved. The code rate R, the number
of information symbols transmitted per channel use, for a length N repetition
code, is R = 1/N. The code rate of a repetition code tends to zero as N is
increased which makes it not very interesting in practice. A more efficient coding
scheme is to encode K > 1 information symbols together to generate a codeword
of length N.
Example 1.3.1 A rate R = 4/7 error correcting code
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A codeword v = (v0, v1, v2, v3, v4, v5, v6) of length N = 7 is generated for a group
of K = 4 information bits u = (u0, u1, u2, u3) as
u = (u0, u1, u2, u3)v = (u0, u1, u2, u3, u0 + u1 + u2, u1 + u2 + u3, u0 + u1 + u3).
(1.4)
The addition here is over the binary field GF(2). The code is defined by the
2K = 16 different codewords corresponding to the 2K different information blocks
u. Since there are N = 7 code bits for K = 4 information bits, the code rate is
R = 4/7. 2
A code of the form described in Example 1.3.1, where codewords are generated
for a block of information symbols, is called a block code. In the dissertation we
only focus on linear block codes over the binary field GF(2). An (N, K) linear
block code of rate R = K/N is a K dimensional vector space of the vector space
GF(2)N. The code in Example 1.3.1 is a (7, 4) linear block code.
For a linear code, the codeword corresponding to an information block can be
conveniently specified by means of matrix multiplication as v1N = u1KGKN,
where G is a generator matrix. In Example 1.3.1, the generator matrix is
G =
1 0 0 0 1 0 1
0 1 0 0 1 1 1
0 0 1 0 1 1 0
0 0 0 1 0 1 1
(47).
(1.5)
Any set ofK basis vectors can be used to obtain a generator matrix. The differentgenerator matrices specify the same code but the mapping of information blocks
to codewords is different. A systematic generator matrix is one where the K
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information symbols appear as a part of the codeword. The generator matrix in
(1.5) is systematic.
The Hamming weight of a vector x is the number of non-zero components in
x. The Hamming distance between two vectors x and y is the Hamming weightof the vector xy = x+y.2 The minimum distance dmin of a block code C is theminimum of the Hamming distances between two distinct codewords in C. For alinear block code C, the sum of any two codewords is also a codeword, and hencedmin is equal to the minimum Hamming weight over all non-zero codewords in C.The minimum distance of a code plays a significant role in determining the error
correcting capability of the code; a code with minimum distance dmin can correct
up to (dmin 1)/2 errors3 on the BSC. The code of Example 1.3.1 has dmin = 3.In general, determining the minimum distance of a code is difficult, especially if
the number of codewords is large.
A linear block code can also be specified by means of a parity-check matrix.
A parity-check matrix H specifies the dual space of the code so that for any
codeword v we have
vHT = 0, (1.6)
where HT denotes the transpose ofH. Thus, for a generator matrix G of the
block code, GHT = 0. For an (N, K) linear block code of rate R = K/N, the
parity-check matrix is an L N matrix, where L = (N K). The 3 7 matrix
H=
1 1 1 0 1 0 0
0 1 1 1 0 1 01 1 0 1 0 0 1
(37)
(1.7)
2Over the binary field addition and subtraction are identical.3z is the largest integer less than or equal to z
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is a parity-check matrix for the code of Example 1.3.1. As in the case of the
generator matrix, any set of (N K) basis vectors of the dual space can be usedto form a parity-check matrix of an (N, K) linear block code. Thus, the same
code can be described by several different parity-check matrices.
1.3.2 Convolutional codes
Convolutional codes were introduced by Elias [8] as an alternative to the class
of block codes. A code sequence of a convolutional code is obtained as the output
of a convolutional encoder, a linear system. Thus, a code sequence is obtained
by the convolution of an information sequence with some generating sequence.
In a convolutional code, the information sequence is continuously encoded into a
code sequence. Further, the output of a convolutional encoder is dependent on
information symbols both at the current and previous times.
Example 1.3.2 A b = 1, c = 2, m = 2, rate R = b/c = 1/2 time-invariant
convolutional encoder.
Figure 1.5 shows a rate R = b/c = 1/2 convolutional encoder. At each time
instant, the encoder maps b = 1 information symbols, ut = u(0)t , into c = 2 code
ut
v(0)t
v(1)t
Figure 1.5. A rate 1/2 convolutional encoder with memory 2.
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symbols, vt = (v(0)t , v
(1)t ). The code symbols are obtained as
v(0)t = u
(0)t
v
(1)
t = u
(0)
t + u
(0)
t1 + u(0)
t2. (1.8)
The encoder stores information symbols from m = 2 time instants past, and hence
we say that the encoder has memory 2. The encoding equations in (1.8) are the
same for each time instant, i.e., the encoder is time invariant. It is also worth
noting that the convolutional encoder is a causal system. 2
A general rate R = b/c convolutional encoder maps an information sequence
u = . . . ,u0,u1, . . . ,ut, . . . , (1.9)
where ut = (u(0)t , . . . , u
(b1)t ) and u
()t GF(2) into a code sequence
v = . . . ,v0,v1, . . . ,vt, . . . , (1.10)
where vt = (v(0)t , . . . , v(c1)t ) and v()t GF(2).For a convolutional encoder without feedback, as in Example 1.3.2, the encod-
ing can be described as
vt = utG0(t) + ut1G1(t) + + utmGm(t), (1.11)
where m is the memory of the convolutional code and Gi(t), i = 0, 1, . . . , m,
are binary b c matrices at each t. For a time invariant encoder the matricesGi(t), i = 0, 1, . . . , m, are the same for all t. As in the case of block codes, the
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encoding can be described by a matrix multiplication v = uG, where the matrix
G =
. . .. . .
G0(0) . . . Gm(m)
G0(1) . . . Gm(m + 1). . .
. . .
G0(t) . . . Gm(t + m)
. . .. . .
(1.12)
is called a generator matrix of the code. (The blank area of the matrix is assumed
to be filled with zeros.) For the convolutional code in Example 1.3.2, we have
G0 = (11),G1 = (01), and G2 = (01).4 We note here that, as in the case of
block codes, there are several different possible generator matrices for the same
convolutional code.
For a time invariant convolutional code, the information and code sequences
are commonly expressed in terms of the delay operator D as
u(D) = + u0 + u1D + + utDt + . . .
v(D) = + v0 + v1D + + vtDt + . . . , (1.13)
and we have v(D) = u(D)G(D) where the b c generator matrix G(D) is equalto
G(D) = G0 +G1D + + GmDm. (1.14)
In Example 1.3.2, we have G(D) = [1, 1 + D + D2] = [1 1] + D[1 0] + [0 1]D2.
For a time invariant convolutional encoder without feedback, G(D) consists of
polynomials. In general, however, G(D) consists of rational functions.
4Since the matrices Gi(t), i = 0, 1, 2, are the same for all t we omit the argument t.
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The minimum of the Hamming distances between two distinct code sequences
of a convolutional code is called the free distance dfree of the code. Since a con-
volutional code is linear, dfree is equal to the minimum Hamming weight over all
non-zero code sequences. As with block codes, on a BSC the transmitted sequencecan be recovered as long as there are no more than (dfree 1)/2 symbols in error.
1.4 Channel decoding
Suppose that the code sequence5 v corresponding to an information sequence
u is transmitted over a discrete channel with binary inputs. The function of the
channel decoder is to compute an estimate u of the information sequence u basedon the received sequence r. Thus, the reliability of a coding scheme is determined
not only by the code but also by the decoding algorithm. An optimal decoding
algorithm is one that minimizes the probability of decoding error.
1.4.1 Optimal decoding
The decoding block error probability PB of a block code is the probability that
the information sequence is decoded incorrectly, i.e.,
PB = P(u = u) = r
P(u = u|r)P(r). (1.15)Since, P(r) is independent of the decoding algorithm, PB is minimized by mini-
mizing P(
u = u|r) or equivalently maximizing P(
u = u|r) for each r. Thus PB
5Whenever the distinction between block and convolutional codes is not relevant, we also use
the term sequence in connection with an information block or codeword of a block code.
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is minimized by choosing u as
u = arg max
uP(u|r). (1.16)
Such a decoder is called a maximum a-posteriori(MAP) decoder. If all information
blocks u are equally likely, then a MAP decoder is equivalent to a maximum
likelihood (ML) decoder which chooses u asu = arg max
uP(r|u). (1.17)
In the case of convolutional codes, ML decoding can be efficiently performed ona trellis [6][7] using the Viterbi [9][10] algorithm. For equally likely information
sequences, the ML decoder minimizes the probability of the decoded sequence
being in error.
An alternative to minimizing the error probability of the complete sequence
or block is to minimize the decoding error probability of a single information bit.
The probability that an information bit ui is decoded incorrectly is called the bit
error probability Pb = P(ui = ui). A symbol-by-symbol MAP decoder chooses uias
ui = arg maxu{0,1}
P(ui = u|r). (1.18)
1.4.2 Iterative decoding
In his noisy channel coding theorem, Shannon showed that randomly con-
structed codes with long block lengths, decoded using an ML decoder, achieve
reliable communication at rates equal to the capacity of the channel. One main
drawback to a practical implementation of such a coding scheme is that ML decod-
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ing becomes prohibitively complex for long block lengths. Very often sub-optimal
decoding algorithms which provide a reasonable tradeoff between complexity and
performance are used in practice.
An attractive way to construct long powerful codes is by concatenating severalsmaller codes. While optimal decoding of the long code is still too complex, it can
be decoded in an iterative fashion. Decoding is carried out by performing optimal
decoding separately for the component codes and then exchanging information be-
tween the component decoders. The performance of the overall decoder is greatly
improved if the component decoders exchange soft reliability values rather than
hard decision values. The component decoders typically calculate the a-posteriori
probabilities (APP)
P(ui = u|r) =
vC:ui=uP(v|r), u {0, 1} (1.19)
using an APP decoder. Note that if the estimated symbol ui is chosen as theargument that maximizes (1.19), then we obtain the MAP decoder.
In the last few years, coding schemes like turbo codes [11] with iterative de-coding have led to the design of systems that operate at rates approaching the
capacity of AWGN channels. In the dissertation we deal with the low-density
parity-check (LDPC) codes, invented by Gallager [12], which are another class of
codes that can operate close to the capacity limit on many channels.
1.5 Outline of the thesis
In the next chapter, we describe some background material on LDPC codes.
We also review the iterative decoding algorithms used for decoding LDPC codes.
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The following three chapters constitute the design component of the thesis. In
Chapter 3, a construction technique for LDPC convolutional codes starting from
algebraically constructed quasi-cyclic LDPC codes is presented. The next chapter
generalizes the code construction technique and a large class of time invariantLDPC convolutional codes are obtained. We indicate techniques to optimize the
code design for improved performance. In this chapter, we also present a bound
on the free distance of time invariant LDPC convolutional codes which shows
that they are not asymptotically good. Chapter 5 begins with a description of
the sliding window decoder for LDPC convolutional codes. We then compare
the sliding window decoder with the decoder of LDPC block codes. The chapter
concludes with a description of a decoding architecture in analog to decode LDPC
convolutional codes.
Chapter 6 and 7 serve as the analysis component of the thesis. In Chapter
6, we construct an ensemble of LDPC convolutional codes and show that these
codes are asymptotically good. This result is analogous to Gallagers result for
LDPC block codes. Chapter 7 describes the iterative decoding analysis of the
ensemble of convolutional codes of Chapter 6 on the BEC channel. We show
that the convolutional nature of the codes leads to significantly better thresholds
compared to the corresponding LDPC block codes. Chapter 8 summarizes the
results of the dissertation and has pointers for future work.
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CHAPTER 2
BACKGROUND
In this chapter we review several concepts and terminology related to LDPC
codes which shall be useful in the remainder of the thesis. Section 2.1 begins with
a brief historical sketch of LDPC codes. We then define an LDPC code and point
out how it may also be represented by means of a Tanner graph. In the next
section iterative decoding of LDPC codes based on message passing algorithms is
described. Section 2.3 deals with LDPC convolutional codes. The Tanner graph
representation of a convolutional code and the features that distinguish it from the
Tanner graph representation of a block code are also pointed out in this section.
2.1 LDPC block codes
LDPC codes and iterative decoding were invented by Gallager in 1963 [12].
With the notable exception of Zyablov and Pinsker [13], and Tanner [14], iterative
coding systems were all but forgotten until the introduction of turbo codes [11].
In the wake of the discovery of turbo codes LDPC codes were rediscovered by
Mackay and Neal [15]. After the rediscovery of LDPC codes the task of analyzing
and designing LDPC codes with performance close to capacity has been carried
out in earnest, e.g., [16][17]. Perhaps the culmination of this process was the
construction of an LDPC code capable of achieving reliable performance within
0.0045 dB of the capacity limit of the binary input AWGN channel [18].
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An LDPC block code is a code defined by a sparse parity-check matrix; con-
taining mostly 0s and relatively few 1s. The LDPC codes introduced by Gallager
have the additional property that the number of ones in each row and each column
of the parity-check matrix is fixed. We call such codes regular LDPC codes.
Definition 2.1.1 A regular LDPC block (N,J,K) code is a code defined by an
L N parity-check matrixH, L < N, with exactly J ones in each column (J
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Symbol Nodes
Constraint Nodes
Figure 2.1. The Tanner graph of the parity-check matrix ofExample 2.1.1. The Tanner graph consists of 20 symbol nodes, eachwith J = 3 edges and 15 constraint nodes, each with K = 4 edges.
It is easy to check that there are four ones in each row and three ones in each
column so that the parity-check matrix defines a (20, 3, 4) code. H has two
dependent rows and the rate of this code is R = 7/20. 2
In his landmark paper [14], Tanner described how a code can be represented by
means of a bipartite graph. The bipartite graph representation, called a Tanner
graph, consists ofsymbol nodescorresponding to the columns ofH and constraint
nodescorresponding to the rows ofH. There is an edge connecting a symbol node
to a constraint node if the entry in the corresponding column and row ofH is a
one. Figure 2.1 shows the Tanner graph corresponding to the parity-check matrix
in Example 2.1.1. There is a one to one correspondence between a parity-check
matrix and its Tanner graph representation. LDPC codes by virtue of their sparse
parity-check matrices have sparse Tanner graph representations. As we shall see,
the Tanner graph provides a convenient setting to describe iterative decoding
algorithms.
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Gallager, in his dissertation [12], showed the existence of LDPC codes with
minimum distance increasing linearly with block length N. Specifically, Gallager
used an ensemble of (J, K) regular LDPC codes and proved that for large N almost
all codes in this ensemble satisfy dmin G(J, K)N provided J > 2. This resultparallels the well known Gilbert-Varshamov (GV) bound [19][20] for the ensemble
of linear binary block codes. The GV bound shows the existence of block codes of
rate R satisfying dmin GV(R)N for large N. Typically, the Gallager coefficientG(J, K) is smaller than the corresponding GV coefficient, thus indicating that
LDPC code ensembles are not as good as the ensemble of randomly constructed
codes. However, it is computationally feasible to efficiently decode long block
length LDPC codes which is not the case for the class of randomly constructed
codes. A linear increase in distance is a remarkable property and does not hold
for many classes of codes that are useful in practice.
For a code defined by means of a parity-check matrix H it is possible to find
the generator matrix G by means of Gaussian elimination. Encoding can then
be carried out as usual by means of matrix multiplication, which has complex-
ity, O(N2), i.e., quadratic in the block length. In [21], a graph based encoding
procedure exploiting the sparse parity-check matrix of LDPC block codes was de-
scribed. In this case encoding complexity can be reduced to O(N + g2) where g
is typically much smaller than N.
2.2 Iterative decoding techniques
One of the most attractive features of LDPC codes is that they allow for effi-
cient iterative decoding. There are several algorithms known for iterative decoding
of LDPC codes; Gallagers bit flipping algorithm [12], the belief propagation (BP)
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decoder, the min-sum decoder etc. Most of these decoding techniques can be
described as message passing algorithms.
The operation of a message passing algorithm can be conveniently described
on the Tanner graph of a code. In a message passing algorithm, messages areexchanged between nodes along the edges of the Tanner graph and nodes process
the incoming messages received via their adjacent edges1 to determine the outgoing
messages. A message along an edge represents the estimate of the bit represented
by the symbol node associated with the edge. The message can be in the form of a
hard decision, i.e., 0 or 1, a probability vector [p0, p1], where pi is the probability of
the bit taking the value i, a log likelihood ratio (LLR) log(p0/p1) etc. An iteration
of message passing consists of a cycle of information passing and processing. In
a message passing algorithm, the outgoing message is calculated based on the
incoming messages along the otheredges. The exact fashion in which the outgoing
message is calculated depends on the message passing algorithm being used.
The computations in a message passing decoder are localized, e.g., computa-
tions at a constraint node are performed independent of the overall structure of
the code. This implies that highly parallel implementations of a message passing
decoder are feasible. Further, the computations are distributed, i.e., computations
are performed by all nodes in the graph. The number of messages exchanged in
an iteration of message passing is dependent on the number of edges in the Tanner
graph; a sparser Tanner graph means lesser computations. Thus, it is computa-
tionally feasible to use message passing algorithms for decoding long block length
LDPC codes.
The most widely used message passing algorithm is the belief propagation(BP)
algorithm. The processing at the symbol and constraint nodes for a BP algorithm
1For symbol nodes we also treat the information from the channel as an incoming message.
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with LLRs as messages is now described. Let ci denote the LLR of the bit as-
sociated with the symbol node i, rij denote the message from symbol node i to
constraint node j, and qji the message from constraint node j to symbol node i.
Let N(i) denote the set of neighbors of the node i and N(i)\j the set of neighborsof node i excluding j. Then the outgoing messages calculated at a constraint node
j and a symbol node i are
qji = log
1 +
iN(j)\i tanh(rij )
1 iN(j)\i tanh(rij )
, i N(j), (2.1)
rij = ci +
jN(i)\jqji, j N(i), (2.2)
respectively. An iteration of decoding involves the above computations at all the
constraint and symbol nodes, respectively, of the Tanner graph. For a BP decoder
with a maximum of I iterations the process is repeated I times. At the start of
decoding the outgoing messages from all symbol nodes are initialized to the LLR
of the corresponding bit. When the I decoding iterations are complete a final
LLR, ci +
jN(i)qji , is computed and used for making a hard decision on the bit
corresponding to symbol node i.
The BP decoder is known to be optimal for Tanner graphs with no closed
paths. However, almost all interesting LDPC Tanner graphs have closed paths or
cycles. Inspite of this, the BP decoder has been observed to work remarkably well
in these cases. Low-complexity versions of the BP algorithm that provide good
complexity to performance tradeoffs also exist. One common modification is to
simplify the computations at the constraint nodes (2.1). Another possibility is toquantize the LLRs. Other examples of message passing are presented in [16].
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Gallager in his thesis [12] analyzed an iterative hard-decision decoding algo-
rithm to upper bound the bit error probability for regular LDPC codes on a
binary symmetric channel (BSC)2. This analysis was extended to a broader class
of channels and message passing algorithms in [16][17][22]. In particular, densityevolution [16] makes analysis of message passing algorithms with messages be-
longing to a continuous alphabet, as for example in the case of the BP algorithm,
feasible. All known techniques for the analysis of message passing algorithms re-
quire that the messages exchanged during iterations be independent. It is possible
to show the existence of a sequence of codes with increasing block length for which
this holds [12][16]. Thus, the analysis is asymptotic in the block length. In the
case of finite block lengths, where arbitrary number of independent iterations is
not possible, little is known about the performance of message passing algorithms.
The concept of a threshold or iterative decoding limit was defined in [16].
The threshold depends on the particular class of codes, the iterative decoding
algorithm, and the channel3. It is the largest channel parameter for which the
probability of an incorrect message being exchanged, when decoding a code from
the specified class with the given decoding algorithm, approaches zero as the
number of iterations tends to infinity.
In [23], it was observed that LDPC codes with irregular Tanner graph4 struc-
tures lead to significantly better performance on the BEC compared to regular
LDPC codes. Following this idea, in [17] density evolution was used to optimize
the degree profile of irregular LDPCs and achieve thresholds close to capacity on
a variety of channels. It should be noted however that the thresholds, especially
2A BSC has two inputs and two outputs identical to the two inputs. The probability that anerror occurs is the same for both inputs.
3Thresholds are known to exist if the channel is physically degraded and the decoding algo-rithm respects this ordering [16].
4A Tanner graph which is not regular is irregular.
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for irregular LDPCs, are channel parameters below which the bit error probability
and not necessarily the block error probability can be made arbitrarily small.
2.3 LDPC convolutional codes
In this section we turn our attention to LDPC convolutional codes, first con-
structed in [24]. We begin with the definition of the syndrome former of a convo-
lutional code.
A rate R = b/c convolutional code can be defined as the set of sequences
v = (. . . ,v0,v1, . . . ,vt, . . . ), vt Fc2, satisfying the equality vHT = 0, where
HT =
. . . . . .
HT0 (0) . . . HTms (ms)
. . . . . .
HT0 (t) . . . HTms (t + ms)
. . . . . .
. (2.3)
The infinite transposed parity-check matrix HT is also called the syndrome for-
mer. The sub-matrices HTi (t + i), i = 1, . . . , ms, are c (c b) binary matrices.The largest i such that HTi (t + i) is a non-zero matrix for some t is called the syn-
drome former memory ms. As in the case of block codes, for a generator matrix
G of the convolutional code we have GHT = 0. The constraint imposed by the
syndrome former can be written as
vtHT0 (t) + vt1HT1 (t) + + vtmsHTms (t) = 0, t Z. (2.4)
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As we shall see in the next chapter, (2.4) can be used for encoding a convolutional
code.
For time invariant convolutional codes, the sub-matrices HTi (t+i), i = 1, . . . , ms,
are the same for all t. For example, the convolutional code in Example 1.3.2is defined by the syndrome former with ms = 2, and H0(t) = (11),H1(t) =
(10), and H2(t) = (10) for all t. In the time invariant case it is possible to express
the syndrome former in terms of the delay operator. The syndrome former for the
code of Example 1.3.2 in terms of the delay operator is H(D)T = [1+ D + D2, 1]T.
Note that we have G(D)H(D)T = 0.
Analogous to LDPC block codes, LDPC convolutional codes are convolutional
codes defined by sparse parity-check matrices or equivalently sparse syndrome
former matrices. Corresponding to definition 2.1.1 of the previous section we have
Definition 2.3.1 A regular LDPC convolutional(ms, J , K ) code is a code defined
by a syndrome formerHT, having exactly J ones in each row, where J
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(a) (b)(b)
(c) (d)
0
000
00
0
0
0
000
000
00
000
00
0
0
0
000
000
00
0 0
0
0
000
0
0
0
0
000
0
000
000
00
0
0 000 00
0
0
0
000
0
000
0
00
111
111
111
111
111
11
11
111
111
111
111
11
11
1 1
1
1
1
11
11
11
1111
1
11
1
11
111
111
1111
111
111
1111
111
1
111
1111
11
1
1
1
1
1
1
1
11
...
...
Figure 2.2. Illustration of the Jimenez-Zigangirov method to constructthe syndrome former of a b = 1, c = 2 LDPC convolutional (4, 3, 6) code
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sub-matrices) as illustrated in Fig. 2.3(a). Then the lower part is moved to the
right as shown in Fig. 2.3(b). Then, fixed c (c b) sub-matrices are appendedfrom the left (Fig. 2.3(c)) to ensure that the matrices H0(t)
T have full rank. The
resulting matrix represents one period and is repeated indefinitely (Fig. 2.3(d)),leading to a syndrome former HT of memory ms = 4, and J = 3 ones in each row
and K = 6 ones in each column. 2
We now turn our attention to the Tanner graph representation of convolu-
tional codes. The Tanner graph of a convolutional code can be obtained as in the
case of block codes. There are symbol nodes corresponding to each row of the
syndrome former HT and constraint nodes corresponding to each column ofHT.
There is an edge connecting a symbol node to a constraint node if the entry in
the corresponding row and column ofHT is a one. As earlier, there is a one to
one correspondence between a syndrome former and its Tanner graph represen-
tation. The features unique to convolutional codes make the Tanner graph of a
convolutional code different in several important ways from that of a block code.
We illustrate these differences by means of an example.
Example 2.3.2 Tanner graph representation of a b = 1, c = 2, LDPC convolu-
tional (3, 2, 3) code
Consider the time invariant convolutional code with syndrome former matrix
HT(D) =
1 D3
D D2
D3 1
(2.5)
It is easy to see that this code has ms = 3, J = 2, and K = 3. Fig. 2.3.2 shows
the Tanner graph of this convolutional code.
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ms
t t + 1 t + 2 t + 3
Figure 2.3. The Tanner graph for the convolutional code ofExample 2.3.2.
The first difference between the Tanner graph of a convolutional code from
that of a block code comes from the fact that the convolutional code is infinite
so that the corresponding Tanner graph is also infinite. The code symbols of a
convolutional code have a time index associated with them. The convolutional
code of this example is a b = 1, c = 3, convolutional code. The symbol nodes
corresponding to the c = 3 symbols generated at each time t are shown vertically
aligned in Fig. 2.3.2. Similarly, at each time t there are c b = 2 parity-checkequations on symbols, the corresponding constraint nodes are shown vertically
aligned in Fig. 2.3.2. The indexing of the Tanner graph by time is absent in the
case of block codes.
The memory of a convolutional code restricts the local structure in the Tanner
graph. Specifically, the memory determines the maximum separation between
two symbol nodes connected by a single constraint node. Two pairs of symbolnodes ms = 3 time units apart (time indices t and t + 3) are shown highlighted in
Fig. 2.3.2. On the other hand, in the Tanner graph of a block code two symbol
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nodes connected by a constraint node can be arbitrarily separated. Note also that
the causality of the convolutional code is reflected by all symbols being connected
only to constraint nodes either at the same or later time instants. Finally, since
the code is time invariant the connectivity of nodes in Tanner graph is the samefor all t. 2
Typically LDPC convolutional codes have large ms (or c b) so that decodingbased on their trellis representation is no longer feasible. However, LDPC convolu-
tional codes by virtue of their sparse syndrome formers have sparse Tanner graph
representations. Thus, they can be decoded by means of message passing algo-
rithms. The special features of the Tanner graph structure of a convolutional code
pointed out here, as we shall see in Chapter 5, lead to a highly parallel continuous
sliding window decoder for LDPC convolutional codes. Further, in Chapter 7 we
explain how the structure of the convolutional code Tanner graphs result in better
thresholds for LDPC convolutional codes compared to corresponding LDPC block
codes.
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CHAPTER 3
LDPC CONVOLUTIONAL CODES CONSTRUCTED FROM QUASI-CYCLIC
CODES
The construction of periodically time varying LDPC convolutional codes has
been described in [24]. One big difficulty in implementing time varying LDPC
convolutional codes is the huge storage requirements for storing a period of the
parity-check matrix. For time invariant convolutional codes the connectivity of
nodes in the Tanner graph structure at different times is identical. This leads to
significantly reduced storage requirements for implementing these codes. In this
chapter we describe the construction of time invariant LDPC convolutional codes
from algebraically constructed quasi-cyclic (QC) LDPC codes.
Section 3.1 begins with a description of the construction technique for the QC
LDPC codes. The QC codes are constructed by generalizing the code construc-
tion technique of a (155, 64) QC LDPC code by Tanner [25]. This is followed
by a description of the design technique for constructing time invariant convolu-
tional codes from the quasi-cyclic codes. Section 3.2 details the properties of the
constructed codes - both quasi-cyclic and convolutional, and the corresponding
Tanner graphs. The relationship between the QC and corresponding convolu-
tional codes, girth and distance properties, and syndrome former based encoding
of the convolutional code are described. The chapter concludes with performance
results of the codes with iterative decoding.
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3.1 Code construction
This section describes the means by which the underlying structure of multi-
plicative groups in the set of integers modulo m may be used to construct low-
density parity-check codes both block codes and convolutional codes.
3.1.1 Construction of quasi-cyclic LDPC block codes
We use the structure of multiplicative groups in the set of integers modulo m to
place circulant matrices within a parity-check matrix so as to form regular quasi-
cyclic LDPC block codes with a variety of block lengths and rates. For prime m,
the integers {0, 1, . . . , m1} form a field under addition and multiplication modulom i.e., the Galois field GF(m). The non-zero elements of GF(m) form a cyclic
multiplicative group. Let a and b be two non-zero elements with multiplicative
orders o(a) = K and o(b) = J respectively1. Then we form the J K matrix Pof elements from GF(m) that has as its (s, t)th element Ps,t = b
sat as follows:
P =
1 a a2 . . . aK1
b ab a2b . . . aK1b
. . . . . . . . . . . . . . .
bJ1 abJ1 a2bJ1 . . . aK1bJ1
. (3.1)
(Here, 0 s J 1 and 0 t K 1.)The LDPC code is constructed by specifying its parity-check matrixH. Specif-
ically, H is made up of a J K array of circulant sub-matrices as shown below:1There exists such elements if K and J divide (m) = m 1, the order of the multiplicative
group.
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H=
I1 Ia Ia2 . . . IaK1
Ib Iab Ia2b . . . IaK1b
. . . . . . . . . . . . . . .
IbJ1 IabJ1 Ia2bJ1 . . . IaK1bJ1
. (3.2)
where Ix is an m m identity matrix with rows cyclically shifted to the left byx 1 positions. The circulant sub-matrix in position (s, t) within H is obtainedby cyclically shifting the rows of the identity matrix to the left by Ps,t 1 places.The resulting binary parity-check matrix is of size Jm Km, which means theassociated code has a rate R 1(J/K). (The rate may be greater than 1(J/K)due to linear dependence among the rows of H; it is easy to see that there are
in fact at least J 1 dependent rows in H.) By construction, every column ofH contains J ones and every row contains K ones, and so H represents a (J, K)
regular LDPC code. (We observe here that for the case J = 2, our construction
yields the graph-theoretic error-correcting codes proposed by Hakimi et al. in
[26].)
The codes constructed using this technique are quasi-cyclic with period K
i.e., cyclically shifting a codeword by one position within each of the K blocks
of circulant sub-matrices (each block consists of m code bits) results in another
codeword2. As described in [27] the construction can also be extended to non-
2Strictly speaking, the word quasi-cyclic means the code has the property that when a
codeword is cyclically shifted by K positions another codeword is obtained; to observe thisproperty in the codes constructed above, the bit positions in each codeword must be permutedto a different order than the one indicated by the construction.
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prime m. Examples of LDPC quasi-cyclic codes constructed in this manner from
a prime m are shown in Table 3.1.1 [28].
Example 3.1.1 A [155,64,20] QC code (m = 31) [25]
Elements a = 2, b = 5 are chosen from GF(31); then o(a) = 5, o(b) = 3, and the
parity-check matrix is given by
H=
I1 I2 I4 I8 I16
I5 I10 I20 I9 I18
I25 I19 I7 I14 I28
(93155)
,
where Ix is a 3131 identity matrix with rows shifted cyclically to the left by x1positions. The parity-check matrix H describes a (3, 5) regular LDPC code and
has rank 91 (determined using Gaussian elimination), so that the corresponding
code has rate R = 64/155 .4129 code. The Tanner graph resulting from H isshown in Figure 3.1. The length of the shortest cycle (i.e., the girth) of the Tanner
graph is eight. The sparse Tanner graph along with the large girth for a code of
this size makes the code ideal for graph-based message-passing decoding. The
associated code has minimum distance dmin = 20 (determined using MAGMA)
that compares well with the minimum distance dmin = 28 of the best linear code
known with the same rate and block length. It can be shown that the Tanner
graph of this code has diameter six, which is the best possible for a (3 , 5) regular
bipartite graph of this size. Also, the Tanner graph has girth eight, while an upper
bound on girth is 10 (from the tree bound; see Section 3.2). 2
Example 3.1.2 A [21,8,6] QC code (m = 7).
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constraint nodes (3 rings of 31 each)
code bit nodes (5 rings of 31 each)
Figure 3.1. Tanner graph for a [155,64,20] QC code.
Elements a = 2, b = 6 are chosen from GF(7); then o(a) = 3, o(b) = 2, and the
parity-check matrix is given by
H=
I1 I2 I4I6 I5 I3
(1421)
,
where Ix is a 7 7 identity matrix with rows cyclically shifted to the left byx 1 positions. The resulting Tanner graph has girth twelve and is shown in
Figure 3.2. (Figure 3.2 shows both the ring-like structure characteristic of theseconstructions and a flattened representation that will be useful in what follows.)
The associated code has minimum distance dmin = 6 and is a (2, 3) regular LDPC
code. The best [21, 8] linear code has dmin = 8. 2
Example 3.1.3 A [5219,4300] QC code (m = 307).
Elements a = 9, b = 17 are chosen from GF(307) (note that 307 is a prime); then
o(a) = 17, o(b) = 3, and the parity-check matrix is given by
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Figure 3.2. Tanner graph for a [21,8,6] QC code.
H=
I1 I9 I81 I115 I114 I105 I24 I216 I102 I304 I280 I64 I269 I272 I299 I235 I273
I17 I153 I149 I113 I96 I250 I101 I295 I199 I256 I155 I167 I275 I19 I171 I4 I36
I289 I145 I77 I79 I97 I259 I182 I103 I6 I54 I179 I76 I70 I16 I144 I68 I305
,
where Ix is a 307 307 identity matrix with rows cyclically shifted to the left byx 1 positions. H is a 921 5219 matrix and describes a (3, 17) regular LDPCcode with minimum distance upper bounded by 24 (see Section 3.2). 2
These examples show that the construction technique described above yields codes
with a wide range of rates and block lengths.
3.1.2 Construction of LDPC convolutional codes
An LDPC convolutional code can be constructed by replicating the constraint
structure of the quasi-cyclic LDPC block code to infinity [29]. Naturally, the
parity-check matrices (or, equivalently, the associated Tanner graphs) of the con-
volutional and quasi-cyclic codes form the key link in this construction.
Each circulant in the parity-check matrix of a QC block code can be specified
by a unique polynomial; the polynomial represents the entries in the first column
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of the circulant matrix. For example, a circulant matrix whose first column is
[1 1 1 0 1 0]T is represented by the polynomial 1+D+D2+D4. Thus, the JmKmbinary parity-check matrix of a regular LDPC code obtained from the construction
described above can be expressed in polynomial form (with indeterminate D) toobtain the following J K matrix:
H(D) =
D0 Da1 Da21 . . . Da
K11
Db1 Dab1 Da2b1 . . . Da
K1b1
. . . . . . . . . . . . . . .
DbJ1
1 DabJ1
1 Da2
bJ1
1 . . . DaK1
bJ1
1
(JK)
.
Since all the circulant sub-matrices in the parity-check matrix of the QC LDPC
code are shifted identity matrices, H(D) is comprised solely of monomials; the
exponent ofD indicates how many places the identity matrix was shifted to form
the corresponding circulant sub-matrix. H(D) is the parity-check matrix of a
corresponding LDPC convolutional code in polynomial form. (The indeterminate
D is now interpreted as the delay operator in the convolutional code.) We note
here that the parity-check matrix of the QC code, when written in circulant form,
is over the ring F2[D]/Dm + 1, i.e., the polynomial ring F2[D] modulo the idealDm + 1, whereas the parity-check matrix of the convolutional code is over therational field F2(D).) In all cases that were examined, the rate of the LDPC
convolutional codes obtained from the QC codes was equal to the design rate of
the original QC code, i.e., R = 1 (J/K). This rate is slightly less than the rateof the original QC code. The syndrome former memory of the convolutional code
so obtained satisfies ms m.
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Example 3.1.4 A rate R = 2/5 LDPC convolutional code.
From the [155, 64, 20] QC code in Example 3.1.4, we can obtain a rate R = 2/5
convolutional code with parity-check and generator matrices given by
H(D) =
1 D D3 D7 D15
D4 D9 D19 D8 D17
D24 D18 D6 D13 D27
(35)
(3.3)
G(D) =
a1(D)(D)
a2(D)(D)
a3(D)(D) 1 0
b1(D)
(D)
b2(D)
(D)
b3(D
(D)0 1
(25)
, (3.4)
where
a1(D) = D4(1 + D7 + D10 + D14 + D18 + D29),
a2(D) = D3(1 + D3 + D6 + D18 + D21 + D36),
a3(D) = D7(1 + D4 + D8 + D11 + D15 + D22),
b1(D) = D13(1 + D6 + D14 + D15 + D23 + D28),
b2(D) = D12(1 + D2 + D11 + D21 + D23 + D35),
b3(D) = D21(1 + D3 + D4 + D5 + D10 + D16),
(D) = 1 + D4 + D14 + D25 + D26 + D33.
The generator matrix G(D) was obtained from the parity-check matrix H(D)
using Gaussian- elimination. The syndrome former memory ofH(D) is ms = 28.
We conjecture that the above convolutional code has a free distance dfree of 24. By
choosing one of the information sequences equal to the denominator polynomial,
i.e., 1 + D4 + D14 + D25 + D26 + D33, and the other information sequence as the
all zero sequence, we obtain a code sequence of weight 24. This only provides an
upper bound on the dfree of this convolutional code, but we have been unable to
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find any lower weight code sequences. Interestingly, as we shall see, this is the
same as the upper bound obtained by Mackay and Davey [30] for LDPC matrices
constructed by using non-overlapping permutation matrices that commute with
each other, as is the case here. Further, the results of [29] guarantee that theminimum distance of the QC block code, 20 in this case, provides a lower bound
on the free distance of the associated convolutional code.
The matrix G(D) in (3.4) is not in minimal form. The minimal-basic genera-
tor matrix [7] has the minimum total memory among all equivalent rational and
polynomial generator matrices and is thus of interest. The minimal-basic form of
G(D), which has total memory 23 + 22 = 45, is given by
Gmin(D) =
a1(D) a2(D) a3(D) a4(D) a5(D)b1(D) b
2(D) b
3(D) b
4(D) b
5(D)
(25)
, (3.5)
where
a1(D) = D6 + D10 + D14 + D17 + D18,
a2(D) = D5 + D8 + D9 + D11 + D13 + D14 + D15 + D19 + D21 + D22 + D23,
a3(D) = D9,
a4(D) = D2 + D10 + D13 + D15 + D16 + D18 + D19 + D20 + D21 + D23,
a5(D) = 1 + D + D3 + D9 + D10 + D11 + D12 + D13 + D15,
b1(D) = D4 + D5 + D6 + D8 + D10 + D11 + D12 + D13 + D14 + D16 + D17,
b2(D) = D3+D4+D5+D6+D8+D11+D12+D13+D14+D16+D17+D18+D19+D20,
b3(D) = D7 + D8 + D9 + D12,
b4(D) = 1+D+D2 +D5 +D8 +D9 +D10 +D11 +D13 +D14 +D15 +D16 +D21 +D22,
b5(D) = 1 + D2 + D4 + D7 + D8 + D13 + D14. 2
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By pulling out common factors from the matrix H(D) in (3.3) we obtain the
matrix
Hequiv(D) =
1 D D3 D7 D15
1 D5 D15 D4 D3
D18 D12 1 D7 D21
(35)
. (3.6)
It is easy to see that the matrices H(D) and Hequiv(D) describe the same code.
However, Hequiv(D) has syndrome former memory, ms = 21 as against ms = 28
for H(D).
Several different LDPC convolutional codes can be obtained from the same QC
LDPC block code. For example, reordering the rows of the parity-check matrix
of the quasi-cyclic code within each block of circulant sub-matrices will leave it
unchanged but can lead to a completely different LDPC convolutional code 3.
Consider once again the [155, 64, 20] QC code. Cyclically shift the first block of
m = 31 rows above by one position, the middle block of 31 rows by five positions,
and the last block of 31 rows by 25 positions, so that now the first row in each
block has a one in the first column. The resulting LDPC matrix is given by
H=
I1 I2 I4 I8 I16
I1 I6 I16 I5 I14
I1 I26 I14 I21 I4
(93155)
,
where again Ix is a 31 31 identity matrix with rows cyclically shifted to the leftby x 1 positions. Clearly, the QC block code and its associated Tanner graph
3
Here we are only interested in those convolutional codes that have the same graph connec-tivity (i.e., nodes of the same degrees) as the base QC block code. For example, equivalentrepresentations for the QC block code can be obtained by suitable linear combinations of rowsofH. However, in such a case, the new representation(s) of the block code and that of thecorresponding convolutional code will not in general have the same node degrees as the originalrepresentation of the QC code.
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are unaffected by these row shifts. However, the convolutional code obtained by
following the above procedure has the parity-check matrix
H1(D) =
1 D D3 D7 D15
1 D5 D15 D4 D13
1 D25 D13 D20 D3
(35)
.
Looking at the third constraint equation ofH(D) and H1(D), we see that that the
two codes are in fact different. (Note that the first and second constraint equations
ofH(D) and H1(D) are equivalent, since the first and second rows ofH(D) are
just delayed versions of the first and second rows ofH1(D) respectively.)
Example 5: A rate 1/3 LDPC convolutional code.From the QC code of Example 2, a rate 1/3 convolutional code with the
following parity-check and generator matrices is obtained
H(D) =
1 D D3
D5 D4 D2
(23)
1 D D3
D3 D2 1
(23)G(D) =
D+D3
1+D2+D4 1D2
1+D2+D4
(13)
,
In this case, the syndrome former memory4 is ms = 3 and the minimal basic
generator matrix has total memory four. The convolutional code has a free
distance dfree = 6, equal to the minimum distance of the original QC code.
In this manner, it is possible to construct numerous convolutional codes with
a sparse Tanner graph representation. For example, for every entry in Table 1,
4The parity-check matrix with common factors removed from the second row.
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it is possible to construct a convolutional code with an actual rate equal to the
design rate indicated in the table.
3.2 Properties of constructed codes
This section describes the properties of the codes constructed in the previous
section. Specifically, the structure of the Tanner graphs, the minimum distance
of the codes, and encoding techniques are described.
3.2.1 Relation between the block and convolutional Tanner graphs
The Tanner graphs for the rate 1/3 convolutional code in Example 5 and thecorresponding QC code (Example 2) are shown in Figure 3.3. We observe that
the Tanner graph of the convolutional code is strikingly similar to that of the QC
code. The convolutional code Tanner graph can be viewed as being obtained by
unwrapping that of the QC code. The construction technique described in the
previous section thus unwraps the Tanner graph of the QC code to obtain the
convolutional code.
Quasi-cyclic code Convolutional code
Figure 3.3. Tanner graph for the rate 1/3 convolutional code ofExample 5.
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3.2.2 Quasi-cyclic block codes viewed as tail-biting convolutional codes
Tail biting is a technique by which a convolutional code can be used to con-
struct a block code without any loss of rate [31][32]. An encoder for the tail-biting
convolutional code is obtained by reducing each polynomial entry in the gener-
ator matrix of the convolutional code modulo Dm + 1 for some positive integer
m and replacing each of the entries so obtained with circulant matrices of size
m m. The block length of the block code5 so derived depends on m. Since thegenerator matrix consists of circulant sub-matrices, the tail-biting convolutional
code obtained is a QC block code. However, this tail-biting code has a rate equal
to that of the convolutional code which is less than or equal to the rate of the
original QC code. As expected, the two QC codes, i.e., the original QC code and
the tail-biting convolutional code, are closely related, as the following theorem
shows.
Theorem 3.2.1 Let C be a length Km QC code with the Jm Km parity checkmatrixH, whereH is composed of m m circulants (i.e., its period is K). Let
C be a convolutional code obtained by unwrapping H. Then the QC block code
(tail-biting convolutional code) C of length Km constructed from C is a sub-codeof C.
Proof: Since the tail-biting code C is quasi-cyclic with period K, any codeword inC can be described by a set of polynomials in the ring F2[D]/Dm + 1. Thereforeany codeword in
C is of the form a(D)G(D) mod Dm + 1, where G(D) is the
generator matrix of the convolutional code C and a(D) is an information poly-
nomial. Now, we know that the polynomial generator matrix of the convolutional
5With a feedforward encoder for the convolutional code, a tail-biting code of any block lengthmay be obtained [6].
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code C satisfies the parity constraints imposed by its parity-check matrix H(D),
i.e., G(D)HT(D) = 0. Therefore, G(D)HT(D) 0 mod Dm + 1. Since, byconstruction, H(D) is also the parity-check matrix of the original QC block code
C, with the entries in H(D) now interpreted as being in the ringF
2[D]/Dm
+ 1,any codeword in C satisfies the constraints of the original QC code C, i.e., thetail-biting code C is a sub-code ofC. 2
If there were no rank reduction in the parity-check matrix of the original QC
block code, it would have a rate equal to that of the convolutional code. In such
a case, it is easy to see that the tail-biting convolutional code would be exactly
the same as the original QC block code.
We have derived a rate 2/5 convolutional code (Example 4) from the [155,64,20]
QC block code (Example 1); from the rate 2/5 convolutional code we can in turn
derive a tail-biting convolutional code, i.e., another QC block code of block length
155 and 62 information bits (rate 2/5), that is a sub-code of the original [155,64,20]
QC block code and has dmin 20.
3.2.3 Girth
The Tanner graph representing the parity-check matrix H in equation (3.2)
cannot have girth less than six6. This is seen by observing that the relative
difference between the shifts across any two columns is different along different
rows. However, the Tanner graph cannot have girth larger than twelve. Figure 3.4
is a graphical illustration of why this is so (see also [33]). The dark diagonal lines
in the figure represent the non-zero entries of the parity-check matrix and theempty regions represent the zeros. The dotted lines indicate a cycle of length
twelve in the Tanner graph involving bit nodes b1 through b6 and constraint
6This holds for prime circulant sizes m but is not necessarily true for non-prime m.
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H =
+X
Y +Z ZX +Y
p5
p6
p4
p3
p2
p1
b2b1 b3 b4 b5 b6
Figure 3.4. A 12-cycle.
nodes p1 through p6. The structure of the circulant sub-matrices in H gives rise
to numerous 12-cycles.
The Tanner graphs of the LDPC convolutional codes constructed herein have
their girth lower bounded by the girth of the corresponding QC LDPC Tanner
graph. For any cycle in the convolutional code Tanner graph we can find an
equivalent cycle in the QC Tanner graph. Say a particular set of bit and constraint
nodes form a cycle in the convolutional code Tanner graph. Then the relative
shifts between the bit nodes sum to zero. The corresponding sequence of bit
and constraint nodes in the QC code (obtained by reducing indices modulo m,
where m is the circulant size in the QC code) have exactly the same relative shifts
(now read modulo m) between the bit nodes, and hence sum to zero i.e., in
the QC code Tanner graph, we find a corresponding cycle of the same length.
However, a cycle in the QC Tanner graph does not always lead to a cycle in
the convolutional Tanner graph. While the relative shifts between bit nodes may
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sum to zero modulo m, the shift may be a non-zero multiple of m, meaning the
corresponding bit nodes in the convolutional code do not form a cycle. Hence,
it is possible that the convolutional code Tanner graph may have a larger girth
than the QC code Tanner graph. Observe, however, that in Figure 3.4 the relativeshifts between bit nodes sum to zero, so that the corresponding bit nodes in the
convolutional code also form a 12 cycle. Hence, the girth of the convolutional
codes is also upper bounded by 12.
3.2.4 Minimum distance
At high SNRs, the maximum likelihood decodin