Low-density graph codes that are optimal for
source/channel coding and binning
Martin J. Wainwright Emin Martinian
Dept. of Statistics, and Tilda Consulting, Inc.
Dept. of Electrical Engineering and Computer Sciences Arlington, MA
University of California, Berkeley emin@alum.mit.edu
wainwrig@{eecs,stat}.berkeley.edu
Technical Report 730,
Department of Statistics, UC Berkeley,
April 2007
Abstract
We describe and analyze the joint source/channel coding properties of a class of sparse
graphical codes based on compounding a low-density generator matrix (LDGM) code with a
low-density parity check (LDPC) code. Our first pair of theorems establish that there exist
codes from this ensemble, with all degrees remaining bounded independently of block length,
that are simultaneously optimal as both source and channel codes when encoding and decoding
are performed optimally. More precisely, in the context of lossy compression, we prove that
finite degree constructions can achieve any pair (R,D) on the rate-distortion curve of the binary
symmetric source. In the context of channel coding, we prove that finite degree codes can achieve
any pair (C, p) on the capacity-noise curve of the binary symmetric channel. Next, we show that
our compound construction has a nested structure that can be exploited to achieve the Wyner-
Ziv bound for source coding with side information (SCSI), as well as the Gelfand-Pinsker bound
for channel coding with side information (CCSI). Although the current results are based on
optimal encoding and decoding, the proposed graphical codes have sparse structure and high
girth that renders them well-suited to message-passing and other efficient decoding procedures.
Keywords: Graphical codes; low-density parity check code (LDPC); low-density generator matrix
code (LDGM); weight enumerator; source coding; channel coding; Wyner-Ziv problem; Gelfand-
Pinsker problem; coding with side information; information embedding; distributed source coding.
1 Introduction
Over the past decade, codes based on graphical constructions, including turbo codes [3] and low-
density parity check (LDPC) codes [17], have proven extremely successful for channel coding prob-
lems. The sparse graphical nature of these codes makes them very well-suited to decoding using
efficient message-passing algorithms, such as the sum-product and max-product algorithms. The
asymptotic behavior of iterative decoding on graphs with high girth is well-characterized by the
density evolution method [25, 39], which yields a useful design principle for choosing degree dis-
tributions. Overall, suitably designed LDPC codes yield excellent practical performance under
iterative message-passing, frequently very close to Shannon limits [7].
http://arxiv.org/abs/0704.1818v1
However, many other communication problems involve aspects of lossy source coding, either
alone or in conjunction with channel coding, the latter case corresponding to joint source-channel
coding problems. Well-known examples include lossy source coding with side information (one
variant corresponding to the Wyner-Ziv problem [45]), and channel coding with side information
(one variant being the Gelfand-Pinsker problem [19]). The information-theoretic schemes achieving
the optimal rates for coding with side information involve delicate combinations of source and
channel coding. For problems of this nature—in contrast to the case of pure channel coding—the
use of sparse graphical codes and message-passing algorithm is not nearly as well understood. With
this perspective in mind, the focus of this paper is the design and analysis sparse graphical codes
for lossy source coding, as well as joint source/channel coding problems. Our main contribution
is to exhibit classes of graphical codes, with all degrees remaining bounded independently of the
blocklength, that simultaneously achieve the information-theoretic bounds for both source and
channel coding under optimal encoding and decoding.
1.1 Previous and ongoing work
A variety of code architectures have been suggested for lossy compression and related problems in
source/channel coding. One standard approach to lossy compression is via trellis-code quantization
(TCQ) [26]. The advantage of trellis constructions is that exact encoding and decoding can be
performed using the max-product or Viterbi algorithm [24], with complexity that grows linearly
in the trellis length but exponentially in the constraint length. Various researchers have exploited
trellis-based codes both for single-source and distributed compression [6, 23, 37, 46] as well as
information embedding problems [5, 15, 42]. One limitation of trellis-based approaches is the
fact that saturating rate-distortion bounds requires increasing the trellis constraint length [43],
which incurs exponential complexity (even for the max-product or sum-product message-passing
algorithms).
Other researchers have proposed and studied the use of low-density parity check (LDPC) codes
and turbo codes, which have proven extremely successful for channel coding, in application to
various types of compression problems. These techniques have proven particularly successful for
lossless distributed compression, often known as the Slepian-Wolf problem [18, 40]. An attractive
feature is that the source encoding step can be transformed to an equivalent noisy channel de-
coding problem, so that known constructions and iterative algorithms can be leveraged. For lossy
compression, other work [31] shows that it is possible to approach the binary rate-distortion bound
using LDPC-like codes, albeit with degrees that grow logarithmically with the blocklength.
A parallel line of work has studied the use of low-density generator matrix (LDGM) codes, which
correspond to the duals of LDPC codes, for lossy compression problems [30, 44, 9, 35, 34]. Focusing
on binary erasure quantization (a special compression problem dual to binary erasure channel
coding), Martinian and Yedidia [30] proved that LDGM codes combined with modified message-
passing can saturate the associated rate-distortion bound. Various researchers have used techniques
from statistical physics, including the cavity method and replica methods, to provide non-rigorous
analyses of LDGM performance for lossy compression of binary sources [8, 9, 35, 34]. In the limit
of zero-distortion, this analysis has been made rigorous in a sequence of papers [12, 32, 10, 14].
Moreover, our own recent work [28, 27] provides rigorous upper bounds on the effective rate-
distortion function of various classes of LDGM codes. In terms of practical algorithms for lossy
binary compression, researchers have explored variants of the sum-product algorithm [34] or survey
propagation algorithms [8, 44] for quantizing binary sources.
1.2 Our contributions
Classical random coding arguments [11] show that random binary linear codes will achieve both
channel capacity and rate-distortion bounds. The challenge addressed in this paper is the design
and analysis of codes with bounded graphical complexity, meaning that all degrees in a factor graph
representation of the code remain bounded independently of blocklength. Such sparsity is critical
if there is any hope to leverage efficient message-passing algorithms for encoding and decoding.
With this context, the primary contribution of this paper is the analysis of sparse graphical code
ensembles in which a low-density generator matrix (LDGM) code is compounded with a low-density
parity check (LDPC) code (see Fig. 2 for an illustration). Related compound constructions have
been considered in previous work, but focusing exclusively on channel coding [16, 36, 41]. In
contrast, this paper focuses on communication problems in which source coding plays an essential
role, including lossy compression itself as well as joint source/channel coding problems. Indeed, the
source coding analysis of the compound construction requires techniques fundamentally different
from those used in channel coding analysis. We also note that the compound code illustrated
in Fig. 2 can be applied to more general memoryless channels and sources; however, so as to bring
the primary contribution into sharp focus, this paper focuses exclusively on binary sources and/or
binary symmetric channels.
More specifically, our first pair of theorems establish that for any rate R ∈ (0, 1), there exist
codes from compound LDGM/LDPC ensembles with all degrees remaining bounded independently
of the blocklength that achieve both the channel capacity and the rate-distortion bound. To the
best of our knowledge, this is the first demonstration of code families with bounded graphical
complexity that are simultaneously optimal for both source and channel coding. Building on
these results, we demonstrate that codes from our ensemble have a naturally “nested” structure,
in which good channel codes can be partitioned into a collection of good source codes, and vice
versa. By exploiting this nested structure, we prove that codes from our ensembles can achieve
the information-theoretic limits for the binary versions of both the problem of lossy source coding
with side information (SCSI, known as the Wyner-Ziv problem [45]), and channel coding with side
information (CCSI, known as the Gelfand-Pinsker [19] problem). Although these results are based
on optimal encoding and decoding, a code drawn randomly from our ensembles will, with high
probability, have high girth and good expansion, and hence be well-suited to message-passing and
other efficient decoding procedures.
The remainder of this paper is organized as follows. Section 2 contains basic background
material and definitions for source and channel coding, and factor graph representations of binary
linear codes. In Section 3, we define the ensembles of compound codes that are the primary
focus of this paper, and state (without proof) our main results on their source and channel coding
optimality. In Section 4, we leverage these results to show that our compound codes can achieve
the information-theoretic limits for lossy source coding with side information (SCSI), and channel
coding with side information (CCSI). Sections 5 and 6 are devoted to proofs that codes from the
compound ensemble are optimal for lossy source coding (Section 5) and channel coding (Section 6)
respectively. We conclude the paper with a discussion in Section 7. Portions of this work have
previously appeared as conference papers [28, 29, 27].
2 Background
In this section, we provide relevant background material on source and channel coding, binary
linear codes, as well as factor graph representations of such codes.
2.1 Source and channel coding
A binary linear code C of block length n consists of all binary strings x ∈ {0, 1}n satisfying a
set of m < n equations in modulo two arithmetic. More precisely, given a parity check matrix
H ∈ {0, 1}m×n, the code is given by the null space
C : = {x ∈ {0, 1}n | Hx = 0} . (1)
Assuming the parity check matrix H is full rank, the code C consists of 2n−m = 2nR codewords,
where R = 1− m
is the code rate.
Channel coding: In the channel coding problem, the transmitter chooses some codeword x ∈ C
and transmits it over a noisy channel, so that the receiver observes a noise-corrupted version Y .
The channel behavior is modeled by a conditional distribution P(y | x) that specifies, for each
transmitted sequence Y , a probability distribution over possible received sequences {Y = y}. In
many cases, the channel is memoryless, meaning that it acts on each bit of C in an independent
manner, so that the channel model decomposes as P(y | x) =
i=1 fi(xi; yi) Here each function
fi(xi; yi) = P(yi | xi) is simply the conditional probability of observing bit yi given that xi was
transmitted. As a simple example, in the binary symmetric channel (BSC), the channel flips each
transmitted bit xi with probability p, so that P(yi | xi) = (1 − p) I[xi = yi] + p (1− I[xi 6= yi]),
where I(A) represents an indicator function of the event A. With this set-up, the goal of the re-
ceiver is to solve the channel decoding problem: estimate the most likely transmitted codeword,
given by x̂ : = argmax
P(y | x). The Shannon capacity [11] of a channel specifies an upper bound
on the rate R of any code for which transmission can be asymptotically error-free. Continuing
with our example of the BSC with flip probability p, the capacity is given by C = 1− h(p), where
h(p) := −p log2 p− (1− p) log2(1− p) is the binary entropy function.
Lossy source coding: In a lossy source coding problem, the encoder observes some source se-
quence S ∈ S, corresponding to a realization of some random vector with i.i.d. elements Si ∼ PS .
The idea is to compress the source by representing each source sequence S by some codeword
x ∈ C. As a particular example, one might be interested in compressing a symmetric Bernoulli
source, consisting of binary strings S ∈ {0, 1}n, with each element Si drawn in an independent
and identically distributed (i.i.d.) manner from a Bernoulli distribution with parameter p = 1
One could achieve a given compression rate R = m
by mapping each source sequence to some
codeword x ∈ C from a code containing 2m = 2nR elements, say indexed by the binary sequences
z ∈ {0, 1}m. In order to assess the quality of the compression, we define a source decoding map
x 7→ Ŝ(x), which associates a source reconstruction Ŝ(x) with each codeword x ∈ C. Given some
distortion metric d : S × S → R+, the source encoding problem is to find the codeword with min-
imal distortion—namely, the optimal encoding x̂ : = argmin
d(Ŝ(x), S). Classical rate-distortion
theory [11] specifies the optimal trade-offs between the compression rate R and the best achievable
average distortion D = E[d(Ŝ, S)], where the expectation is taken over the random source sequences
S. For instance, to follow up on the Bernoulli compression example, if we use the Hamming metric
d(Ŝ, S) = 1
i=1 |Ŝi − Si| as the distortion measure, then the rate-distortion function takes the
form R(D) = 1− h(D), where h is the previously defined binary entropy function.
We now provide definitions of “good” source and channel codes that are useful for future reference.
Definition 1. (a) A code family is a good D-distortion binary symmetric source code if for any
ǫ > 0, there exists a code with rate R < 1− h (D) + ǫ that achieves Hamming distortion less than
or equal to D.
(b) A code family is a good BSC(p)-noise channel code if for any ǫ > 0 there exists a code with
rate R > 1− h (p)− ǫ with error probability less than ǫ.
2.2 Factor graphs and graphical codes
Both the channel decoding and source encoding problems, if viewed naively, require searching over
an exponentially large codebook (since |C| = 2nR for a code of rate R). Therefore, any practically
useful code must have special structure that facilitates decoding and encoding operations. The
success of a large subclass of modern codes in use today, especially low-density parity check (LDPC)
codes [17, 38], is based on the sparsity of their associated factor graphs.
PSfrag replacements
y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12
c1 c2 c3 c4 c5 c6
PSfrag replacements
x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12
z1 z2 z3 z4 z5 z6 z7 z8 z9
(a) (b)
Figure 1. (a) Factor graph representation of a rate R = 0.5 low-density parity check (LDPC) code
with bit degree dv = 3 and check degree d
= 6. (b) Factor graph representation of a rate R = 0.75
low-density generator matrix (LDGM) code with check degree dc = 3 and bit degree dv = 4.
Given a binary linear code C, specified by parity check matrix H, the code structure can be
captured by a bipartite graph, in which circular nodes (◦) represent the binary values xi (or columns
of H), and square nodes (�) represent the parity checks (or rows of H). For instance, Fig. 1(a)
shows the factor graph for a rate R = 1
code in parity check form, with m = 6 checks acting on
n = 12 bits. The edges in this graph correspond to 1’s in the parity check matrix, and reveal the
subset of bits on which each parity check acts. The parity check code in Fig. 1(a) is a regular
code with bit degree 3 and check degree 6. Such low-density constructions, meaning that both the
bit degrees and check degrees remain bounded independently of the block length n, are of most
practical use, since they can be efficiently represented and stored, and yield excellent performance
under message-passing decoding. In the context of a channel coding problem, the shaded circular
nodes at the top of the low-density parity check (LDPC) code in panel (a) represent the observed
variables yi received from the noisy channel.
Figure 1(b) shows a binary linear code represented in factor graph form by its generator matrix
G. In this dual representation, each codeword x ∈ {0, 1}n is generated by taking the matrix-vector
product of the form Gz, where z ∈ {0, 1}m is a sequence of information bits, and G ∈ {0, 1}n×m is
the generator matrix. For the code shown in panel (b), the blocklength is n = 12, and information
sequences are of length m = 9, for an overall rate of R = m/n = 0.75 in this case. The degrees of
the check and variable nodes in the factor graph are dc = 3 and dv = 4 respectively, so that the
associated generator matrix G has dc = 3 ones in each row, and dv = 4 ones in each column. When
the generator matrix is sparse in this setting, then the resulting code is known as a low-density
generator matrix (LDGM) code.
2.3 Weight enumerating functions
For future reference, it is useful to define the weight enumerating function of a code. Given a binary
linear code of blocklength m, its codewords x have renormalized Hamming weights w : =
range in the interval [0, 1]. Accordingly, it is convenient to define a function Wm : [0, 1] → R+ that,
for each w ∈ [0, 1], counts the number of codewords of weight w:
Wm(w) :=
x ∈ C | w =
⌉ }∣∣∣∣ , (2)
where ⌈·⌉ denotes the ceiling function. Although it is typically difficult to compute the weight enu-
merator itself, it is frequently possible to compute (or bound) the average weight enumerator, where
the expectation is taken over some random ensemble of codes. In particular, our analysis in the
sequel makes use of the average weight enumerator of a (dv, d
c)-regular LDPC code (see Fig. 1(a)),
defined as
Am(w; dv , d
c) :=
logE [Wm(w)] , (3)
where the expectation is taken over the ensemble of all regular (dv , d
c)-LDPC codes. For such
regular LDPC codes, this average weight enumerator has been extensively studied in previous
work [17, 22].
3 Optimality of bounded degree compound constructions
In this section, we describe the compound LDGM/LDPC construction that is the focus of this
paper, and describe our main results on their source and channel coding optimality.
3.1 Compound construction
Our main focus is the construction illustrated in Fig. 2, obtained by compounding an LDGM code
(top two layers) with an LDPC code (bottom two layers). The code is defined by a factor graph
with three layers: at the top, a vector x ∈ {0, 1}n of codeword bits is connected to a set of n parity
checks, which are in turn connected by a sparse generator matrix G to a vector y ∈ {0, 1}m of
information bits in the middle layer. The information bits y are also codewords in an LDPC code,
defined by the parity check matrix H connecting the middle and bottom layers.
In more detail, considering first the LDGM component of the compound code, each codeword
x ∈ {0, 1}n in the top layer is connected via the generator matrix G ∈ {0, 1}n×m to an information
sequence y ∈ {0, 1}m in the middle layer; more specifically, we have the algebraic relation x = Gy.
Note that this LDGM code has rate RG ≤
. Second, turning to the LDPC component of the
compound construction, its codewords correspond to a subset of information sequences y ∈ {0, 1}m
in the middle layer. In particular, any valid codeword y satisfies the parity check relation Hy = 0,
where H ∈ {0, 1}m×k joins the middle and bottom layers of the construction. Overall, this defines
an LDPC code with rate RH = 1−
, assuming that H has full row rank.
The overall code C obtained by concatenating the LDGM and LDPC codes has blocklength n,
and rate R upper bounded by RGRH . In algebraic terms, the code C is defined as
C : = {x ∈ {0, 1}n | x = Gy for some y ∈ {0, 1}m such that Hy = 0} , (4)
where all operations are in modulo two arithmetic.
PSfrag replacements
k1 k2
H1 H2
Figure 2. The compound LDGM/LDPC construction analyzed in this paper, consisting of a (n,m)
LDGM code over the middle and top layers, compounded with a (m, k) LDPC code over the middle
and bottom layers. Codewords x ∈ {0, 1}n are placed on the top row of the construction, and are
associated with information bit sequences z ∈ {0, 1}m in the middle layer. The LDGM code over the
top and middle layers is defined by a sparse generator matrix G ∈ {0, 1}n×m with at most dc ones
per row. The bottom LDPC over the middle and bottom layers is represented by a sparse parity
check matrix H ∈ {0, 1}k×m with dv ones per column, and d
ones per row.
Our analysis in this paper will be performed over random ensembles of compound LDGM/LDPC
ensembles. In particular, for each degree triplet (dc, dv, d
c), we focus on the following random
ensemble:
(a) For each fixed integer dc ≥ 4, the random generator matrix G ∈ {0, 1}
n×m is specified as
follows: for each of the n rows, we choose dc positions with replacement, and put a 1 in each
of these positions. This procedure yields a random matrix with at most dc ones per row,
since it is possible (although of asymptotically negligible probability for any fixed dc) that
the same position is chosen more than once.
(b) For each fixed degree pair (dv, d
c), the random LDPC matrix H ∈ {0, 1}
k×m is chosen uni-
formly at random from the space of all matrices with exactly dv ones per column, and exactly
d′c ones per row. This ensemble is a standard (dv , d
c)-regular LDPC ensemble.
We note that our reason for choosing the check-regular LDGM ensemble specified in step (a) is not
that it need define a particularly good code, but rather that it is convenient for theoretical purposes.
Interestingly, our analysis shows that the bounded degree dc check-regular LDGM ensemble, even
though it is sub-optimal for both source and channel coding in isolation [28, 29], defines optimal
source and channel codes when combined with a bottom LDPC code.
3.2 Main results
Our first main result is on the achievability of the Shannon rate-distortion bound using codes from
LDGM/LDPC compound construction with finite degrees (dc, dv , d
c). In particular, we make the
following claim:
Theorem 1. Given any pair (R,D) satisfying the Shannon bound, there is a set of finite degrees
(dc, dv , d
c) and a code from the associated LDGM/LDPC ensemble with rate R that is a D-good
source code (see Definition 1).
In other work [28, 27], we showed that standard LDGM codes from the check-regular ensemble
cannot achieve the rate-distortion bound with finite degrees. As will be highlighted by the proof of
Theorem 1 in Section 5, the inclusion of the LDPC lower code in the compound construction plays
a vital role in the achievability of the Shannon rate-distortion curve.
Our second main result of this result is complementary in nature to Theorem 1, regarding the
achievability of the Shannon channel capacity using codes from LDGM/LDPC compound construc-
tion with finite degrees (dc, dv , d
c). In particular, we have:
Theorem 2. For all rate-noise pairs (R, p) satisfying the Shannon channel coding bound R <
1 − h (p), there is a set of finite degrees (dc, dv , d
c) and a code from the associated LDGM/LDPC
ensemble with rate R that is a p-good channel code (see Definition 1).
To put this result into perspective, recall that the overall rate of this compound construction is
given by R = RGRH . Note that an LDGM code on its own (i.e., without the lower LDPC code)
is a special case of this construction with RH = 1. However, a standard LDGM of this variety is
not a good channel code, due to the large number of low-weight codewords. Essentially, the proof
of Theorem 2 (see Section 6) shows that using a non-trivial LDPC lower code (with RH < 1) can
eliminate these troublesome low-weight codewords.
4 Consequences for coding with side information
We now turn to consideration of the consequences of our two main results for problems of coding
with side information. It is well-known from previous work [47] that achieving the information-
theoretic limits for these problems requires nested constructions, in which a collection of good source
codes are nested inside a good channel code (or vice versa). Accordingly, we begin in Section 4.1
by describing how our compound construction naturally generates such nested ensembles. In Sec-
tions 4.2 and 4.3 respectively, we discuss how the compound construction can be used to achieve
the information-theoretic optimum for binary source coding with side information (a version of the
Wyner-Ziv problem [45]), and binary information embedding (a version of “dirty paper coding”,
or the Gelfand-Pinsker problem [19]).
4.1 Nested code structure
The structure of the compound LDGM/LDPC construction lends itself naturally to nested code
constructions. In particular, we first partition the set of k lower parity checks into two disjoint
subsets K1 and K2, of sizes k1 and k2 respectively, as illustrated in Fig. 2. Let H1 and H2 denote
the corresponding partitions of the full parity check matrix H ∈ {0, 1}k×m. Now let us set all parity
bits in the subset K2 equal to zero, and consider the LDGM/LDPC code C(G,H1) defined by the
generator matrix G and the parity check (sub)matrix H1, as follows
C(G,H1) := {x ∈ {0, 1}
n | x = Gy for some y ∈ {0, 1}m such that H1 y = 0} . (5)
Note that the rate of C(G,H1) is given by R
′ = RGRH1 , which can be suitably adjusted by
modifying the LDGM and LDPC rates respectively. Moreover, by applying Theorems 1 and 2,
there exist finite choices of degree such that C(G,H1) will be optimal for both source and channel
coding.
Considering now the remaining k2 parity bits in the subset K2, suppose that we set them equal
to a fixed binary sequence m ∈ {0, 1}k2 . Now consider the code
C(m) :=
x ∈ {0, 1}n | x = Gy for some y ∈ {0, 1}m such that
. (6)
Note that for each binary sequencem ∈ {0, 1}k2 , the code C(m) is a subcode of C(G,H1); moreover,
the collection of these subcodes forms a disjoint partition as follows
C(G,H1) =
m∈{0,1}k2
C(m). (7)
Again, Theorems 1 and 2 guarantee that (with suitable degree choices), each of the subcodes C(m)
is optimal for both source and channel coding. Thus, the LDGM/LDPC construction has a natural
nested property, in which a good source/channel code—namely C(G,H1)—is partitioned into a
disjoint collection {C(m), m ∈ {0, 1}k1} of good source/channel codes. We now illustrate how this
nested structure can be exploited for coding with side information.
4.2 Source coding with side information
We begin by showing that the compound construction can be used to perform source coding with
side information (SCSI).
4.2.1 Problem formulation
Suppose that we wish to compress a symmetric Bernoulli source S ∼ Ber(1
) so as to be able
to reconstruct it with Hamming distortion D. As discussed earlier in Section 2, the minimum
achievable rate is given by R(D) = 1− h (D). In the Wyner-Ziv extension of standard lossy com-
pression [45], there is an additional source of side information about S—say in the form Z = S ⊕W
where W ∼ Ber(δ) is observation noise—that is available only at the decoder. See Fig. 3 for a block
diagram representation of this problem.
Decoder
PSfrag replacements
Figure 3. Block diagram representation of source coding with side information (SCSI). A source S is
compressed to rate R. The decoder is given the compressed version, and side information Z = S⊕W ,
and wishes to use (Ŝ, Z) to reconstruct the source S up to distortion D.
For this binary version of source coding with side information (SCSI), it is known [2] that the
minimum achievable rate takes the form
RWZ(D, p) = l. c. e.
h (D ∗ p)− h (D) , (p, 0)
, (8)
where l. c. e. denotes the lower convex envelope. Note that in the special case p = 1
, the side
information is useless, so that the Wyner-Ziv rate reduces to classical rate-distortion. In the
discussion to follow, we focus only on achieving rates of the form h (D ∗ p)−h (D), as any remaining
rates on the Wyner-Ziv curve (8) can be achieved by time-sharing with the point (p, 0).
4.2.2 Coding procedure for SCSI
In order to achieve rates of the form R = h (D ∗ p)− h (D), we use the compound LDGM/LDPC
construction, as illustrated in Fig. 2, according to the following procedure.
Step #1, Source coding: The first step is a source coding operation, in which we transform the
source sequence S to a quantized representation S. In order to do so, we use the code C(G,H1), as
defined in equation (5) and illustrated in Fig. 4(a), composed of the generator matrix G and the
parity check matrix H1. Note that C(G,H1), when viewed as a code with blocklength n, has rate
R1 : =
1− k1
= m−k1
. Suppose that we choose1 the middle and lower layer sizes m and k1
respectively such that
m− k1
= 1− h (D) + ǫ/2, (9)
where ǫ > 0 is arbitrary. For any such choice, Theorem 1 guarantees the existence of finite degrees
(dc, dv , d
c) such that that C(G,H1) is a good D-distortion source code. Consequently, for the speci-
fied rate R1, we can use C(G,H1) in order to transform the source to some quantized representation
Ŝ such that the error Ŝ ⊕ S has average Hamming weighted bounded by D. Moreover, since Ŝ is a
codeword of C(G,H1), there is some sequence of information bits Ŷ ∈ {0, 1}
m such that Ŝ = GŶ
and H1Ŷ = 0.
PSfrag replacements
0 0 0
PSfrag replacements
0 0 0 1 0
k1 k2
H1 H2
(a) (b)
Figure 4. (a) Source coding stage for Wyner-Ziv procedure: the C(G,H1), specified by the generator
matrix G ∈ {0, 1}n×m and parity check matrix H1 ∈ {0, 1}
k1×m, is used to quantize the source vector
S ∈ {0, 1}n, thereby obtaining a quantized version Ŝ ∈ {0, 1}n and associated vector of information
bits Ŷ ∈ {0, 1}m, such that Ŝ = G Ŷ and H1 Ŷ = 0.
Step #2. Channel coding: Given the output (Ŷ , Ŝ) of the source coding step, consider the
sequence H2Ŷ ∈ {0, 1}
k2 of parity bits associated with the parity check matrix H2. Transmitting
this string of parity bits requires rate Rtrans =
. Overall, the decoder receives both these k2 parity
bits, as well as the side information sequence Z = S ⊕W . Using these two pieces of information,
the goal of the decoder is to recover the quantized sequence Ŝ.
Viewing this problem as one of channel coding, the effective rate of this channel code is
m−k1−k2
. Note that the side information can be written in the form
Z = S ⊕W = Ŝ ⊕ E ⊕W,
1Note that the choices of m and k1 need not be unique.
where E : = S⊕Ŝ is the quantization noise, andW ∼ Ber(p) is the channel noise. If the quantization
noise E were i.i.d. Ber(D), then the overall effective noise E ⊕W would be i.i.d. Ber(D ∗ p). (In
reality, the quantization noise is not exactly i.i.d. Ber(D), but it can be shown [47] that it can be
treated as such for theoretical purposes.) Consequently, if we choose k2 such that
m− k1 − k2
= 1− h (D ∗ p)− ǫ/2, (10)
for an arbitrary ǫ > 0, then Theorem 2 guarantees that the decoder will (w.h.p.) be able to recover
a codeword corrupted by (D ∗ p)-Bernoulli noise.
Summarizing our findings, we state the following:
Corollary 1. There exist finite choices of degrees (dc, dv, d
c) such that the compound LDGM/LDPC
construction achieves the Wyner-Ziv bound.
Proof. With the source coding rate R1 chosen according to equation (9), the encoder will return a
quantization Ŝ with average Hamming distance to the source S of at most D. With the channel
coding rate R2 chosen according to equation (10), the decoder can with high probability recover
the quantization Ŝ. The overall transmission rate of the scheme is
Rtrans =
m− k1
m− k1 − k2
= R1 − R2
= (1− h (D) + ǫ/2)− (1− h (D ∗ p)− ǫ/2)
= h (D ∗ p)− h (D) + ǫ.
Since ǫ > 0 was arbitrary, we have established that the scheme can achieve rates arbitrarily close
to the Wyner-Ziv bound.
4.3 Channel coding with side information
We now show how the compound construction can be used to perform channel coding with side
information (CCSI).
4.3.1 Problem formulation
In the binary information embedding problem, given a specified input vector V ∈ {0, 1}n, the
channel output Z ∈ {0, 1}n is assumed to take the form
Z = V ⊕ S ⊕W, (11)
where S is a host signal (not under control of the user), and W ∼ Ber(p) corresponds to channel
noise. The encoder is free to choose the input vector V ∈ {0, 1}n, subject to an average channel
constraint
E [‖V ‖1] ≤ w, (12)
for some parameter w ∈ (0, 1
]. The goal is to use a channel coding scheme that satisfies this
constraint (12) so as to maximize the number of possible messages m that can be reliably commu-
nicated. Moreover, We write V ≡ V
to indicate that each channel input is implicitly identified
with some underlying message m. Given the channel output Z = V
⊕ S ⊕W , the goal of the de-
coder is to recover the embedded message m. The capacity for this binary information embedding
problem [2] is given by
RIE(w, p) = u. c. e.
h (w)− h (p) , (0, 0)
, (13)
where u. c. e. denotes the upper convex envelope. As before, we focus on achieving rates of the form
h (w) − h (p), since any remaining points on the curve (13) can be achieved via time-sharing with
the (0, 0) point.
Encoder Decoder
PSfrag replacements
Figure 5. Block diagram representation of channel coding with side information (CCSI). The
encoder embeds a message m into the channel input V
, which is required to satisfy the average
channel constraint 1
‖1] ≤ w. The channel produces the output Z = Vm⊕S⊕W , where S is a
host signal known only to the encoder, and W ∼ Ber(p) is channel noise. Given the channel output
Y , the decoder outputs an estimate m̂ of the embedded message.
4.3.2 Coding procedure for CCSI
In order to achieve rates of the form R = h (w)− h (p), we again use the compound LDGM/LDPC
construction in Fig. 2, now according to the following two step procedure.
Step #1: Source coding: The goal of the first stage is to embed the message into the transmitted
signal V via a quantization process. In order to do so, we use the code illustrated in Fig. 6(a),
specified by the generator matrix G and parity check matrices H1 and H2. The set K1 of k1 parity
bits associated with the check matrix H1 remain fixed to zero throughout the scheme. On the other
hand, we use the remaining k2 lower parity bits associated with H2 to specify a particular message
m ∈ {0, 1}k2 that the decoder would like to recover. In algebraic terms, the resulting code C(m)
has the form
C(m) :=
x ∈ {0, 1}n | x = Gy for some y ∈ {0, 1}m such that
.(14)
Since the encoder has access to host signal S, it may use this code C(m) in order to quantize the
host signal. After doing so, the encoder has a quantized signal Ŝ
∈ {0, 1}n and an associated
sequence Ŷ
∈ {0, 1}m of information bits such that Ŝ
= GŶ
. Note that the quantized signal
) specifies the message m in an implicit manner, since m = H2 Ŷm by construction of the
code C(m).
Now suppose that we choose n,m and k such that
m− k1 − k2
= 1− h (w) + ǫ/2 (15)
for some ǫ > 0, then Theorem 1 guarantees that there exist finite degrees (dc, dv , d
c) such that
the resulting code is a good w-distortion source code. Otherwise stated, we are guaranteed that
w.h.p, the quantization error E : = S ⊕ Ŝ has average Hamming weight upper bounded by wn.
Consequently, we may set the channel input V equal to the quantization noise (V = E), thereby
ensuring that the average channel constraint (12) is satisfied.
Step #2, Channel coding: In the second phase, the decoder is given a noisy channel observation
of the form
Z = E ⊕ S ⊕W = Ŝ ⊕W, (16)
and its task is to recover Ŝ. In terms of the code architecture, the k1 lower parity bits remain set to
zero; the remaining k2 parity bits, which represent the message m, are unknown to the coder. The
resulting code, as illustrated illustrated in Fig. 6(b), can be viewed as channel code with effective
PSfrag replacements
0 0 0 1 0
H1 H2
PSfrag replacements
0 0 0
(a) (b)
Figure 6. (a) Source coding step for binary information embedding. The message m ∈ {0, 1}k2
specifies a particular coset; using this particular source code, the host signal S is compressed to Ŝ,
and the quantization error E = S ⊕ Ŝ is transmitted over the constrained channel. (b) Channel
coding step for binary information embedding. The decoder receives Z = Ŝ ⊕W where W ∼ Ber(p)
is channel noise, and seeks to recover Ŝ, and hence the embedded message m specifying the coset.
rate m−k1
. Now suppose that we choose k1 such that the effective code used by the decoder has
m− k1
= 1− h (p)− ǫ/2, (17)
for some ǫ > 0. Since the channel noise W is Ber(p) and the rate R2 chosen according to (17),
Theorem 2 guarantees that the decoder will w.h.p. be able to recover the pair Ŝ and Ŷ . Moreover,
by design of the quantization procedure, we have the equivalence m = H2 Ŷ so that a simple
syndrome-forming procedure allows the decoder to recover the hidden message.
Summarizing our findings, we state the following:
Corollary 2. There exist finite choices of degrees (dc, dv, d
c) such that the compound LDGM/LDPC
construction achieves the binary information embedding (Gelfand-Pinsker) bound.
Proof. With the source coding rate R1 chosen according to equation (15), the encoder will return
a quantization Ŝ of the host signal S with average Hamming distortion upper bounded by w.
Consequently, transmitting the quantization error E = S ⊕ Ŝ will satisfy the average channel
constraint (12). With the channel coding rate R2 chosen according to equation (17), the decoder
can with high probability recover the quantized signal Ŝ, and hence the message m. Overall, the
scheme allows a total of 2k2 distinct messages to be embedded, so that the effective information
embedding rate is
Rtrans =
m− k1
m− k1 − k2
= R2 − R1
= (1− h (p)− ǫ/2) − (1− h (w) + ǫ/2)
= h (w)− h (p) + ǫ,
for some ǫ > 0. Thus, we have shown that the proposed scheme achieves the binary information
embedding bound (13).
5 Proof of source coding optimality
This section is devoted to the proof of the previously stated Theorem 1 on the source coding
optimality of the compound construction.
5.1 Set-up
In establishing a rate-distortion result such as Theorem 1, perhaps the most natural focus is the
random variable
dn(S,C) :=
‖x− S‖1, (18)
corresponding to the (renormalized) minimum Hamming distance from a random source sequence
S ∈ {0, 1}n to the nearest codeword in the code C. Rather than analyzing this random variable
directly, our proof of Theorem 1 proceeds indirectly, by studying an alternative random variable.
Given a binary linear code with N codewords, let i = 0, 1, 2, . . . , N − 1 be indices for the
different codewords. We say that a codeword Xi is distortion D-good for a source sequence S if the
Hamming distance ‖Xi⊕S‖1 is at most Dn. We then set the indicator random variable Z
i(D) = 1
when codeword Xi is distortion D-good. With these definitions, our proof is based on the following
random variable:
Tn(S,C;D) :=
Zi(D). (19)
Note that Tn(S,C;D) simply counts the number of codewords that are distortion D-good for a
source sequence S. Moreover, for all distortions D, the random variable Tn(S,C;D) is linked to
dn(S,C) via the equivalence
P[Tn(S,C;D) > 0] = P[dn(S,C) ≤ D]. (20)
Throughout our analysis of P[Tn(S,C;D) > 0], we carefully track only its exponential behavior.
More precisely, the analysis to follow will establish an inverse polynomial lower bound of the
form P[Tn(S,C;D) > 0] ≥ 1/f(n) where f(·) collects various polynomial factors. The following
concentration result establishes that the polynomial factors in these bounds can be ignored:
Lemma 1 (Sharp concentration). Suppose that for some target distortion D, we have
P[Tn(S,C;D) > 0] ≥ 1/f(n), (21)
where f(·) is a polynomial function satisfying log f(n) = o(n). Then for all ǫ > 0, there exists a
fixed code C̄ of sufficiently large blocklength n such that E[dn(S; C̄)] ≤ D + ǫ.
Proof. Let us denote the random code C as (C1,C2), where C1 denotes the random LDGM top
code, and C2 denotes the random LDPC bottom code. Throughout the analysis, we condition on
some fixed LDPC bottom code, say C2 = C̄2. We begin by showing that the random variable
(dn(S,C) | C̄2) is sharply concentrated. In order to do so, we construct a vertex-exposure martin-
gale [33] of the following form. Consider a fixed sequential labelling {1, . . . , n} of the top LDGM
checks, with check i associated with source bit Si. We reveal the check and associated source
bit in a sequential manner for each i = 1, . . . , n, and so define a sequence of random variables
{U0, U1, . . . , Un} via U0 : = E[dn(S,C) | C̄2], and
Ui : = E
dn(S,C) | S1, . . . , Si, C̄2
, i = 1, . . . , n. (22)
By construction, we have Un = (dn(S,C) | C̄2). Moreover, this sequence satisfies the following
bounded difference property: adding any source bit Si and the associated check in moving from
Ui−1 to Ui can lead to a (renormalized) change in the minimum distortion of at most ci = 1/n.
Consequently, by applying Azuma’s inequality [1], we have, for any ǫ > 0,
[∣∣(dn(S,C) | C̄2)− E[dn(S,C) | C̄2]
∣∣ ≥ ǫ
≤ exp
. (23)
Next we observe that our assumption (21) of inverse polynomial decay implies that, for at least
one bottom code C̄2,
P[dn(S,C) ≤ D | C̄2] = P[Tn(S,C;D) > 0 | C̄2] ≥ 1/g(n), (24)
for some subexponential function g. Otherwise, there would exist some α > 0 such that
P[Tn(S,C;D) > 0 | C̄2] ≤ exp(−nα)
for all choices of bottom code C̄2, and taking averages would violate our assumption (21).
Finally, we claim that the concentration result (23) and inverse polynomial bound (24) yield
the result. Indeed, if for some ǫ > 0, we had D < E[dn(S,C) | C̄2] − ǫ, then the concentration
bound (23) would imply that the probability
P[dn(S,C) ≤ D | C̄2] ≤ P[dn(S,C) ≤ E[dn(S,C) | C̄2]− ǫ | C̄2]
[∣∣(dn(S,C) | C̄2)− E[dn(S,C) | C̄2]
∣∣ ≥ ǫ
decays exponentially, which would contradict the inverse polynomial bound (24) for sufficiently
large n. Thus, we have shown that assumption (21) implies that for all ǫ > 0, there exists a
sufficiently large n and fixed bottom code C̄2 such that E[dn(S,C) | C̄2] ≤ D + ǫ. If the average
over LDGM codes C1 satisfies this bound, then at least one choice of LDGM top code must also
satisfy it, whence we have established that there exists a fixed code C̄ such that E[dn(S; C̄)] ≤ D+ǫ,
as claimed.
5.2 Moment analysis
In order to analyze the probability P[Tn(S,C;D) > 0], we make use of the moment bounds given
in the following elementary lemma:
Lemma 2 (Moment methods). Given any random variable N taking non-negative integer values,
there holds
(E[N ])
E[N2]
≤ P[N > 0]
≤ E[N ]. (25)
Proof. The upper bound (b) is an immediate consequence of Markov’s inequality, whereas the lower
bound (a) follows by applying the Cauchy-Schwarz inequality [20] as follows
(E[N ])
N I[N > 0]
≤ E[N2] E
2[N > 0]
= E[N2] P[N > 0].
The remainder of the proof consists in applying these moment bounds to the random variable
Tn(S,C;D), in order to bound the probability P[Tn(S,C;D) > 0]. We begin by computing the first
moment:
Lemma 3 (First moment). For any code with rate R, the expected number of D-good codewords
scales exponentially as
logE[Tn] = [R− (1− h (D))] ± o(1). (26)
Proof. First, by linearity of expectation E[Tn] =
∑2nR−1
i=0 P[Z
i(D) = 1] = 2nRP[Z0(D) = 1], where
we have used symmetry of the code construction to assert that P[Zi(D) = 1] = P[Z0(D) = 1] for
all indices i. Now the event {Z0(D) = 1} is equivalent to an i.i.d Bernoulli(1
) sequence of length
n having Hamming weight less than or equal to Dn. By standard large deviations theory (either
Sanov’s theorem [11], or direct asymptotics of binomial coefficients), we have
logP[Z0(D) = 1] = 1− h (D) ± o(1),
which establishes the claim.
Unfortunately, however, the first moment E[Tn] need not be representative of typical behavior
of the random variable Tn, and hence overall distortion performance of the code. As a simple
illustration, consider an imaginary code consisting of 2nR copies of the all-zeroes codeword. Even
for this “code”, as long as R > 1− h (D), the expected number of distortion-D optimal codewords
grows exponentially. Indeed, although Tn = 0 for almost all source sequences, for a small subset of
source sequences (of probability mass ≈ 2−n [1−h(D)]), the random variable Tn takes on the enormous
value 2nR, so that the first moment grows exponentially. However, the average distortion incurred
by using this code will be ≈ 0.5 for any rate, so that the first moment is entirely misleading. In order
to assess the representativeness of the first moment, one needs to ensure that it is of essentially the
same order as the variance, hence the comparison involved in the second moment bound (25)(a).
5.3 Second moment analysis
Our analysis of the second moment begins with the following alternative representation:
Lemma 4.
E[T 2n(D)] = E[Tn(D)]
j 6=0
P[Zj(D) = 1 | Z0(D) = 1]
. (27)
Based on this lemma, proved in Appendix C, we see that the key quantity to control is the condi-
tional probability P[Zj(D) = 1 | Z0(D) = 1]. It is this overlap probability that differentiates the
low-density codes of interest here from the unstructured codebooks used in classical random coding
arguments.2 For a low-density graphical code, the dependence between the events {Zj(D) = 1}
and {Z0(D) = 1} requires some analysis.
Before proceeding with this analysis, we require some definitions. Recall our earlier definition (3)
of the average weight enumerator associated with an (dv , d
c) LDPC code, denoted by Am(w).
Moreover, let us define for each w ∈ [0, 1] the probability
Q(w;D) := P [‖X(w) ⊕ S‖1 ≤ Dn | ‖S‖1 ≤ Dn] , (28)
where the quantity X(w) ∈ {0, 1}n denotes a randomly chosen codeword, conditioned on its under-
lying length-m information sequence having Hamming weight ⌈wm⌉. As shown in Lemma 9 (see
Appendix A), the random codeword X(w) has i.i.d. Bernoulli elements with parameter
δ∗(w; dc) =
1− (1− 2w)dc
. (29)
With these definitions, we now break the sum on the RHS of equation (27) intom terms, indexed
by t = 1, 2, . . . ,m, where term t represents the contribution of a given non-zero information sequence
y ∈ {0, 1}m with (Hamming) weight t. Doing so yields
j 6=0
P[Zj(D) = 1 | Z0(D) = 1] =
Am(t/m)Q(t/m;D)
≤ m max
1≤t≤m
{Am(t/m) Q(t/m;D)}
≤ m max
w∈[0,1]
{Am(w) Q(w;D)} .
Consequently, we need to control both the LDPC weight enumerator Am(w) and the probability
Q(w;D) over the range of possible fractional weights w ∈ [0, 1].
5.4 Bounding the overlap probability
The following lemma, proved in Appendix D, provides a large deviations bound on the probability
Q(w;D).
Lemma 5. For each w ∈ [0, 1], we have
logQ(w;D) ≤ F (δ∗(w; dc);D) + o(1), (30)
2In the latter case, codewords are chosen independently from some ensemble, so that the overlap probability is
simply equal to P[Zj(D) = 1]. Thus, for the simple case of unstructured random coding, the second moment bound
actually provides the converse to Shannon’s rate-distortion theorem for the symmetric Bernoulli source.
where for each t ∈ (0, 1
] and D ∈ (0, 1
], the error exponent is given by
F (t;D) := D log
(1− t)eλ
+ (1−D) log
(1− t) + teλ
− λ∗D. (31)
Here λ∗ : = log
b2−4ac
, where a : = t (1− t) (1−D), b : = (1− 2D)t2, and c : = −t (1− t)D.
In general, for any D ∈ (0, 1
], the function F ( · ;D) has the following properties. At t = 0,
it achieves its maximum F (0 ;D) = 0, and then is strictly decreasing on the interval (0, 1
], ap-
proaching its minimum value − [1− h (D)] as t → 1
. Figure 7 illustrates the form of the function
F (δ∗(ω; dc);D) for two different values of distortion D, and for degrees dc ∈ {3, 4, 5}. Note that
0 0.1 0.2 0.3 0.4 0.5
Weight
Decay of overlap probability: D = 0.1101
0 0.1 0.2 0.3 0.4 0.5
Weight
Decay of overlap probability: D = 0.3160
(a) (b)
Figure 7. Plot of the upper bound (30) on the overlap probability 1
logQ(w;D) for different choices
of the degree dc, and distortion probabilities. (a) Distortion D = 0.1100. (b) Distortion D = 0.3160.
increasing dc causes F (δ
∗(ω; dc);D) to approach its minimum −[1− h (D)] more rapidly.
We are now equipped to establish the form of the effective rate-distortion function for any
compound LDGM/LDPC ensemble. Substituting the alternative form of E[T 2n ] from equation (27)
into the second moment lower bound (25) yields
logP[Tn(D) > 0] ≥
logE[Tn(D)]− log
j 6=0
P[Zj(D) = 1 | Z0(D) = 1]
≥ R− (1− h (D))− max
w∈[0,1]
logAm(w) +
logQ(w;D)
− o(1)
≥ R− (1− h (D))− max
w∈[0,1]
logAm(w)
+ F ( δ∗(w; dc),D)
− o(1), (32)
0 0.1 0.2 0.3 0.4 0.5
Weight
Minimum achievable rates: (R,D) = (0.50, 0.1100)
Compound
Naive LDGM
0 0.1 0.2 0.3 0.4 0.5
0.025
0.075
0.125
Weight
Minimum achievable rates: (R,D) = (0.10, 0.3160)
Compound
Naive LDGM
(a) (b)
Figure 8. Plot of the function defining the lower bound (33) on the minimum achievable rate for a
specified distortion. Shown are curves with LDGM top degree dc = 4, comparing the uncoded case
(no bottom code, dotted curve) to a bottom (4, 6) LDPC code (solid line). (a) Distortion D = 0.1100.
(b) Distortion D = 0.3160.
where the last step follows by applying the upper bound on Q from Lemma 5, and the relation
m = RGn =
n. Now letting B(w; dv , d
c) be any upper bound on the log of average weight
enumerator
logAm(w)
, we can then conclude that 1
log P[Tn(D) > 0] is asymptotically non-negative
for all rate-distortion pairs (R,D) satisfying
R ≥ max
w∈[0,1]
1− h (D) + F (δ∗(w; dc),D)
B(w;dv,d′c)
. (33)
Figure 8 illustrates the behavior of the RHS of equation (33), whose maximum defines the effective
rate-distortion function, for the case of LDGM top degree dc = 4. Panels (a) and (b) show the
cases of distortion D = 0.1100 and D = 0.3160 respectively, for which the respective Shannon rates
are R = 0.50 and R = 0.10. Each panel shows two plots, one corresponding the case of uncoded
information bits (a naive LDGM code), and the other to using a rate RH = 2/3 LDPC code with
degrees (dv , dc) = (4, 6). In all cases, the minimum achievable rate for the given distortion is
obtained by taking the maximum for w ∈ [0, 0.5] of the plotted function. For any choices of D, the
plotted curve is equal to the Shannon bound RSha = 1 − h (D) at w = 0, and decreases to 0 for
w = 1
Note the dramatic difference between the uncoded and compound constructions (LDPC-coded).
In particular, for both settings of the distortion (D = 0.1100 and D = 0.3160), the uncoded curves
rise from their initial values to maxima above the Shannon limit (dotted horizontal line). Con-
sequently, the minimum required rate using these constructions lies strictly above the Shannon
optimum. The compound construction curves, in contrast, decrease monotonically from their max-
imum value, achieved at w = 0 and corresponding to the Shannon optimum. In the following
section, we provide an analytical proof of the fact that for any distortion D ∈ [0, 1
), it is al-
ways possible to choose finite degrees such that the compound construction achieves the Shannon
optimum.
5.5 Finite degrees are sufficient
In order to complete the proof of Theorem 1, we need to show that for all rate-distortion pairs
(R,D) satisfying the Shannon bound, there exist LDPC codes with finite degrees (dv , d
c) and a
suitably large but finite top degree dc such that the compound LDGM/LDPC construction achieves
the specified (R,D).
Our proof proceeds as follows. Recall that in moving from equation (32) to equation (33), we
assumed a bound on the average weight enumerator Am of the form
logAm(w) ≤ B(w; dv , d
c) + o(1). (34)
For compactness in notation, we frequently write B(w), where the dependence on the degree pair
(dv , d
c) is understood implicitly. In the following paragraph, we specify a set of conditions on this
bounding function B, and we then show that under these conditions, there exists a finite degree dc
such that the compound construction achieves specified rate-distortion point. In Appendix F, we
then prove that the weight enumerator of standard regular LDPC codes satisfies the assumptions
required by our analysis.
Assumptions on weight enumerator bound We require that our bound B on the weight
enumerator satisfy the following conditions:
A1: the function B is symmetric around 1
, meaning that B(w) = B(1− w) for all w ∈ [0, 1].
A2: the function B is twice differentiable on (0, 1) with B′(1
) = 0 and B′′(1
) < 0.
A3: the function B achieves its unique optimum at w = 1
, where B(1
) = RH .
A4: there exists some ǫ1 > 0 such that B(w) < 0 for all w ∈ (0, ǫ1), meaning that the ensemble
has linear minimum distance.
In order to establish our claim, it suffices to show that for all (R,D) such that R > 1− h (D),
there exists a finite choice of dc such that
w∈[0,1]
+ F (δ∗(w; dc),D)
︸ ︷︷ ︸
 ≤ R− [1− h (D)] : = ∆ (35)
K(w; dc)
Restricting to even dc ensures that the function F is symmetric about w =
; combined with as-
sumption A2, this ensures that K is symmetric around 1
, so that we may restrict the maximization
to [0, 1
] without loss of generality. Our proof consists of the following steps:
(a) We first prove that there exists an ǫ1 > 0, independent of the choice of dc, such that
K(w; dc) ≤ ∆ for all w ∈ [0, ǫ1].
(b) We then prove that there exists ǫ2 > 0, again independent of the choice of dc, such that
K(w; dc) ≤ ∆ for all w ∈ [
− ǫ2,
(c) Finally, we specify a sufficiently large but finite degree d∗c that ensures the conditionK(w; d
c) ≤ ∆
for all w ∈ [ǫ1, ǫ2].
5.5.1 Step A
By assumption A4 (linear minimum distance), there exists some ǫ1 > 0 such that B(w) ≤ 0 for all
w ∈ [0, ǫ1]. Since F (δ
∗(w; dc);D) ≤ 0 for all w, we have K(w; dc) ≤ 0 < ∆ in this region. Note
that ǫ1 is independent of dc, since it specified entirely by the properties of the bottom code.
5.5.2 Step B
For this step of the proof, we require the following lemma on the properties of the function F :
Lemma 6. For all choices of even degrees dc ≥ 4, the function G(w; dc) = F (δ
∗(w; dc),D) is
differentiable in a neighborhood of w = 1
, with
; dc) = − [1− h (D)] , G
; dc) = 0, and G
; dc) = 0. (36)
See Appendix E for a proof of this claim. Next observe that we have the uniform bound
G(w; dc) ≤ G(w; 4) for all dc ≥ 4 and w ∈ [0,
]. This follows from the fact that F (u;D) is
decreasing in u, and that δ∗(w; 4) ≤ δ∗(w; dc) for all dc ≥ 4 and w ∈ [0,
]. Since B is independent
of dc, this implies that K(w; dc) ≤ K(w; 4) for all w ∈ [0,
]. Hence it suffices to set dc = 4, and
show that K(w; 4) ≤ ∆ for all w ∈ [1
− ǫ2,
]. Using Lemma 6, Assumption A2 concerning the
derivatives of B, and Assumption A4 (that B(1
) = RH), we have
; 4) = R− [1− h (D)] = ∆,
; 4) =
R B′(1
; 4) = 0, and
K ′′(
; 4) =
R B′′(1
+G′′(
; 4) =
R B′′(1
By the continuity of K ′′, the second derivative remains negative in a region around 1
, say for all
w ∈ [1
− ǫ2,
] for some ǫ2 > 0. Then, for all w ∈ [
− ǫ2,
], we have for some w̃ ∈ [w, 1
] the second
order expansion
K(w; 4) = K(
; 4) +K ′(
; 4)(w −
K(w̃; 4)
K(w̃; 4)
Thus, we have established that there exists an ǫ2 > 0, independent of the choice of dc, such that
for all even dc ≥ 4, we have
K(w; dc) ≤ K(w, 4) ≤ ∆ for all w ∈ [
− ǫ2,
]. (37)
5.5.3 Step C
Finally, we need to show that K(w; dc) ≤ ∆ for all w ∈ [ǫ1, ǫ2]. From assumption A3 and the
continuity of B, there exists some ρ(ǫ2) > 0 such that
B(w) ≤ RH [1− ρ(ǫ2)] for all w ≤
− ǫ2. (38)
From Lemma 6, limu→ 1
F (u;D) = F (1
;D) = − [1− h (D)]. Moreover, as dc → +∞, we have
δ∗(ǫ1; dc) →
. Therefore, for any ǫ3 > 0, there exists a finite degree d
c such that
F (δ∗(ǫ1; d
c);D) ≤ − [1− h (D)] + ǫ3.
Since F is non-increasing in w, we have F (δ∗(w; d∗c);D) ≤ − [1− h (D)] + ǫ3 for all w ∈ [ǫ1, ǫ2].
Putting together this bound with the earlier bound (38) yields that for all w ∈ [ǫ1, ǫ2]:
K(w; dc) = R
+ F (δ∗(w; d∗c),D)
≤ R [1− ρ(ǫ2)]− [1− h (D)] + ǫ3
= {R− [1− h (D)]}+ (ǫ3 − Rρ(ǫ2))
= ∆ + (ǫ3 − Rρ(ǫ2))
Since we are free to choose ǫ3 > 0, we may set ǫ3 =
Rρ(ǫ2)
to yield the claim.
6 Proof of channel coding optimality
In this section, we turn to the proof of the previously stated Theorem 2, concerning the channel
coding optimality of the compound construction.
If the codeword x ∈ {0, 1}n is transmitted, then the receiver observes V = x ⊕ W , where W
is a Ber(p) random vector. Our goal is to bound the probability that maximum likelihood (ML)
decoding fails where the probability is taken over the randomness in both the channel noise and
the code construction. To simplify the analysis, we focus on the following sub-optimal (non-ML)
decoding procedure. Let ǫn be any non-negative sequence such that ǫn/n → 0 but ǫ
n/n → +∞—say
for instance, ǫn = n
Definition 2 (Decoding Rule:). With the threshold d(n) := pn+ ǫn, decode to codeword xi ⇐⇒
‖xi ⊕ V ‖1 ≤ d(n), and no other codeword is within d(n) of V .
The extra term ǫn in the threshold d(n) is chosen for theoretical convenience. Using the following
two lemmas, we establish that this procedure has arbitrarily small probability of error, whence ML
decoding (which is at least as good) also has arbitrarily small error probability.
Lemma 7. Using the suboptimal procedure specified in the definition (2), the probability of decoding
error vanishes asymptotically provided that
RG B(w)−D (p||δ
∗(w; dc) ∗ p) < 0 for all w ∈ (0,
], (39)
where B is any function bounding the average weight enumerator as in equation (34).
Proof. Let N = 2nR = 2mRH denote the total number of codewords in the joint LDGM/LDPC
code. Due to the linearity of the code construction and symmetry of the decoding procedure, we
may assume without loss of generality that the all zeros codeword 0n was transmitted (i.e., x = 0n).
In this case, the channel output is simply V = W and so our decoding procedure will fail if and
only if one the following two conditions holds:
(i) either ‖W‖1 > d(n), or
(ii) there exists a sequence of information bits y ∈ {0, 1}m satisfying the parity check equation
Hy = 0 such that the codeword Gy satisfies ‖Gy ⊕W‖1 ≤ d(n).
Consequently, using the union bound, we can upper bound the probability of error as follows:
perr ≤ P[‖W‖1 > d(n)] +
‖Gyi ⊕W‖1 ≤ d(n)
. (40)
Since E[‖W‖1] = pn, we may apply Hoeffdings’s inequality [13] to conclude that
P[‖W‖1 > d(n)] ≤ 2 exp
→ 0 (41)
by our choice of ǫn. Now focusing on the second term, let us rewrite it as a sum over the possible
Hamming weights ℓ = 1, 2, . . . ,m of information sequences (i.e., ‖y‖1 = ℓ) as follows:
‖Gyi ⊕W‖1 ≤ d(n)
‖Gy ⊕W‖1 ≥ d(n)
∣∣ ‖y‖1 = ℓ
where we have used the fact that the (average) number of information sequences with fractional
weight ℓ/m is given by the LDPC weight enumerator Am(
). Analyzing the probability terms
in this sum, we note Lemma 9 (see Appendix A) guarantees that Gy has i.i.d. Ber(δ∗( ℓ
; dc))
elements, where δ∗( · ; dc) was defined in equation (29). Consequently, the vector Gy⊕W has i.i.d.
Ber(δ( ℓ
) ∗ p) elements. Applying Sanov’s theorem [11] for the special case of binomial variables
yields that for any information bit sequence y with ℓ ones, we have
‖Gy ⊕W‖1 ≥ d(n)
∣∣ ‖y‖1 = ℓ
≤ f(n)2−nD(p||δ(
)∗p), (42)
for some polynomial term f(n). We can then upper bound the second term in the error bound (40)
‖Gyi ⊕W‖1 ≤ d(n)
≤ f(m) exp
1≤ℓ≤m
) + o(m)− nD
p||δ(
) ∗ p
where we have used equation (42), as well as the assumed upper bound (34) on Am in terms of B.
Simplifying further, we take logarithms and rescale by m to assess the exponential rate of decay,
thereby obtaining
‖Gyi ⊕W‖1 ≤ d(n)
≤ max
1≤ℓ≤m
p||δ(
) ∗ p
+ o(1)
≤ max
w∈[0,1]
B(w)−
D (p||δ(w) ∗ p)
+ o(1),
and establishing the claim.
Lemma 8. For any p ∈ (0, 1) and total rate R : = RGRH < 1 − h (p), there exist finite choices
of the degree triplet (dc, dv , d
c) such that (39) is satisfied.
Proof. For notational convenience, we define
L(w) := RGB(w)−D (p||δ
∗(w; dc) ∗ p) . (43)
First of all, it is known [17] that a regular LDPC code with rate RH =
< 1 and dv ≥ 3 has linear
minimum distance. More specifically, there exists a threshold ν∗ = ν∗(dv, dc) such that B(w) ≤ 0
for all w ∈ [0, ν∗]. Hence, since B(w)−D (p||δ∗(w; dc) ∗ p) ≥ 0 for all w ∈ (0, 1), for w ∈ (0, ν
∗], we
have L(w) < 0.
Turning now to the interval [ν∗, 1
], consider the function
L̃(w) := Rh (w)−D (p||δ∗(w; dc) ∗ p) . (44)
Since B(w) ≤ RHh (w), we have L(w) ≤ L̃(w), so that it suffices to upper bound L̃. Observe that
) = R − (1 − h (p)) < 0 by assumption. Therefore, it suffices to show that, by appropriate
choice of dc, we can ensure that L̃(w) ≤ L̃(
). Noting that L̃ is infinitely differentiable, calculating
derivatives yields L̃′(1
) = 0 and L̃′′(1
) < 0. (See Appendix G for details of these derivative
calculations.) Hence, by second order Taylor series expansion around w = 1
, we obtain
L̃(w) = L̃(
L̃′′(w̄)(w −
where w̄ ∈ [w, 1
]. By continuity of L̃′′, we have L̃′′(w) < 0 for all w in some neighborhood of 1
so that the Taylor series expansion implies that L̃(w) ≤ L̃(1
) for all w in some neighborhood, say
(µ, 1
It remains to bound L̃ on the interval [ν∗, µ]. On this interval, we have L̃(w) ≤ Rh (µ) −
D (p||δ∗(ν∗; dc) ∗ p). By examining equation (29) from Lemma 9, we see that by choosing dc
sufficiently large, we can make δ∗(ν∗; dc) arbitrarily close to
, and hence D (p||δ∗(ν∗; dc) ∗ p)
arbitrarily close to 1 − h (p). More precisely, let us choose dc large enough to guarantee that
D (p||δ∗(ν∗; dc) ∗ p) < (1 − ǫ) (1 − h (p)), where ǫ =
R (1−h(µ))
1−h(p) . With this choice, we have, for all
w ∈ [ν∗, µ], the sequence of inequalities
L̃(w) ≤ Rh (µ)−D (p||δ∗(ν∗; dc) ∗ p)
< Rh (µ)−
(1− h (p))−R(1− h (µ))
= R− (1− h (p)) < 0,
which completes the proof.
7 Discussion
In this paper, we established that it is possible to achieve both the rate-distortion bound for
symmetric Bernoulli sources and the channel capacity for the binary symmetric channel using
codes with bounded graphical complexity. More specifically, we have established that there exist
low-density generator matrix (LDGM) codes and low-density parity check (LDPC) codes with
finite degrees that, when suitably compounded to form a new code, are optimal for both source
and channel coding. To the best of our knowledge, this is the first demonstration of classes of codes
with bounded graphical complexity that are optimal as source and channel codes simultaneously.
We also demonstrated that this compound construction has a naturally nested structure that can
be exploited to achieve the Wyner-Ziv bound [45] for lossy compression of binary data with side
information, as well as the Gelfand-Pinsker bound [19] for channel coding with side information.
Since the analysis of this paper assumed optimal decoding and encoding, the natural next step
is the development and analysis of computationally efficient algorithms for encoding and decoding.
Encouragingly, the bounded graphical complexity of our proposed codes ensures that they will, with
high probability, have high girth and good expansion, thus rendering them well-suited to message-
passing and other efficient decoding procedures. For pure channel coding, previous work [16, 36, 41]
has analyzed the performance of belief propagation when applied to various types of compound
codes, similar to those analyzed in this paper. On the other hand, for pure lossy source coding, our
own past work [44] provides empirical demonstration of the feasibility of modified message-passing
schemes for decoding of standard LDGM codes. It remains to extend both these techniques and their
analysis to more general joint source/channel coding problems, and the compound constructions
analyzed in this paper.
Acknowledgements
The work of MJW was supported by National Science Foundation grant CAREER-CCF-0545862,
a grant from Microsoft Corporation, and an Alfred P. Sloan Foundation Fellowship.
A Basic property of LDGM codes
For a given weight w ∈ (0, 1), suppose that we enforce that the information sequence y ∈ {0, 1}m
has exactly ⌈wm⌉ ones. Conditioned on this event, we can then consider the set of all codewords
X(w) ∈ {0, 1}n, where we randomize over low-density generator matrices G chosen as in step (a)
above. Note for any fixed code, X(w) is simply some codeword, but becomes a random variable
when we imagine choosing the generator matrix G randomly. The following lemma characterizes
this distribution as a function of the weight w and the LDGM top degree dc:
Lemma 9. Given a binary vector y ∈ {0, 1}m with a fraction w of ones, the distribution of
the random LDGM codeword X(w) induced by y is i.i.d. Bernoulli with parameter δ∗(w; dc) =
1− (1− 2w)dc
Proof. Given a fixed sequence y ∈ {0, 1}m with a fraction w ones, the random codeword bit Xi(w)
at bit i is formed by connecting dc edges to the set of information bits.
3 Each edge acts as an i.i.d.
Bernoulli variable with parameter w, so that we can write
Xi(w) = V1 ⊕ V2 ⊕ . . .⊕ Vdc , (45)
where each Vk ∼ Ber(w) is independent and identically distributed. A straightforward calculation
using z-transforms (see [17]) or Fourier transforms over GF (2) yields that Xi(w) is Bernoulli with
parameter δ∗(w; dc) as defined.
B Bounds on binomial coefficients
The following bounds on binomial coefficients are standard (see Chap. 12, [11]):
log(n+ 1)
. (46)
Here, for α ∈ (0, 1), the quantity h(α) := −α logα − (1 − α) log(1 − α) is the binomial entropy
function.
3In principle, our procedure allows two different edges to choose the same information bit, but the probability of
such double-edges is asymptotically negligible.
C Proof of Lemma 4
First, by the definition of Tn(D), we have
E[T 2n(D)] = E
Zi(D)Zj(D)
= E[Tn] +
j 6=i
P[Zi(D) = 1, Zi(D) = 1].
To simplify the second term on the RHS, we first note that for any i.i.d Bernoulli(1
) sequence
S ∈ {0, 1}n and any codeword Xj , the binary sequence S′ : = S ⊕ Xj is also i.i.d. Bernoulli(1
Consequently, for each pair i 6= j, we have
Zi(D) = 1, Zj(D) = 1
‖Xi ⊕ S‖1 ≤ Dn, ‖X
j ⊕ S‖1 ≤ Dn
‖Xi ⊕ S′‖1 ≤ Dn, ‖X
j ⊕ S′‖1 ≤ Dn
‖Xi ⊕Xj ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
Note that for each j 6= i, the vector Xi ⊕Xj is a non-zero codeword. For each fixed i, summing
over j 6= i can be recast as summing over all non-zero codewords, so that
i 6=j
Zi(D) = 1, Zj(D) = 1
j 6=i
‖Xi ⊕Xj ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
k 6=0
‖Xk ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
= 2nR
k 6=0
‖Xk ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
= 2nRP
Z0(D) = 1
k 6=0
Zk(D) = 1 | Z0(D) = 1
= E[Tn]
k 6=0
Zk(D) = 1 | Z0(D)
thus establishing the claim.
D Proof of Lemma 5
We reformulate the probability Q(w,D) as follows. Recall that Q involves conditioning the source
sequence S on the event ‖S‖1 ≤ Dn. Accordingly, we define a discrete variable T with distribution
P(T = t) =
) for t = 0, 1, . . . ,Dn,
representing the (random) number of 1s in the source sequence S. Let Ui and Vj denote Bernoulli
random variables with parameters 1 − δ∗(w; dc) and δ
∗(w; dc) respectively. With this set-up, con-
ditioned on codeword j having a fraction wn ones, the quantity Q(w,D) is equivalent to the
probability that the random variable
W : =
i=1 Uj +
j=1 Vj if T ≥ 1
j=1 Vj if T = 0
is less than Dn. To bound this probability, we use a Chernoff bound in the form
logP[W ≤ Dn] ≤ inf
logMW (λ)− λD
. (48)
We begin by computing the moment generating function MW . Taking conditional expectations and
using independence, we have
MW (λ) =
P[T = t] [MU (λ)]
[MV (λ)]
Here the cumulant generating functions have the form
logMU(λ) = log
(1− δ)eλ + δ
, and (49a)
logMV (λ) = log
(1− δ) + δeλ
, (49b)
where we have used (and will continue to use) δ as a shorthand for δ∗(w; dc).
Of interest to us is the exponential behavior of this expression in n. Using the standard entropy
approximations to the binomial coefficient (see Appendix B), we can bound MW (λ) as
− h (D) +
logMU (λ) +
logMV (λ)
︸ ︷︷ ︸
, (50)
where f(n) denotes a generic polynomial factor. Further analyzing this sum, we have
g(t) ≤
0≤t≤Dn
log g(t) +
log f(n)
log(nD)
= max
0≤t≤Dn
− h (D) +
logMU (λ) +
logMV (λ)
+ o(1)
≤ max
u∈[0,D]
{h (u)− h (D) + u logMU (λ) + (1− u) logMV (λ)} + o(1).
Combining this upper bound on 1
logMW (λ) with the Chernoff bound (48) yields that
logP[W ≤ Dn] ≤ inf
u∈[0,D]
G(u, λ; δ) + o(1) (51)
where the function G takes the form
G(u, λ; δ) := h (u)− h (D) + u logMU (λ) + (1− u) logMV (λ)− λD. (52)
Finally, we establish that the solution (u∗, λ∗) to the min-max saddle point problem (51) is
unique, and specified by u∗ = D and λ∗ as in Lemma 5. First of all, observe that for any δ ∈ (0, 1),
the function G is continuous, strictly concave in u and strictly convex in λ. (The strict concavity
follows since h (u) is strictly concave with the remaining terms linear; the strict convexity follows
since cumulant generating functions are strictly convex.) Therefore, for any fixed λ < 0, the
maximum over u ∈ [0,D] is always achieved. On the other hand, for any D > 0, u ∈ [0,D] and
δ ∈ (0, 1), we have G(u;λ; t) → +∞ as λ → −∞, so that the infimum is either achieved at some
λ∗ < 0, or at λ∗ = 0. We show below that it is always achieved at an interior point λ∗ < 0. Thus
far, using standard saddle point theory [21], we have established the existence and uniqueness of
the saddle point solution (u∗, λ∗).
To verify the fixed point conditions, we compute partial derivatives in order to find the optimum.
First, considering u, we compute
(u, λ; δ) = log
+ logMU (λ)− logMV (λ)
= log
+ log
(1− δ)eλ + δ
− log
(1− δ) + δeλ
Solving the equation ∂G
(u, λ; δ) = 0 yields
exp(λ)
1 + exp(λ)
1 + exp(λ)
(1−D) ≥ 0. (53)
Since D ≤ 1
, a bit of algebra shows that u′ ≥ D for all choices of λ. Since the maximization is
constrained to [0,D], the optimum is always attained at u∗ = D.
Turning now to the minimization over λ, we compute the partial derivative to find
(u, λ; δ) = u
(1− δ) exp(λ)
(1− δ) exp(λ) + δ
+ (1− u)
δ exp(λ)
(1− δ) + δ exp(λ)
Setting this partial derivative to zero yields a quadratic equation in exp(λ) with coefficients
a = δ (1− δ) (1 −D) (54a)
b = u(1− δ)2 + (1− u)δ2 −D
δ2 + (1− δ)2
. (54b)
c = −Dδ(1− δ). (54c)
The unique positive root ρ∗ of this quadratic equation is given by
ρ∗(δ,D, u) :=
b2 − 4ac
. (55)
It remains to show that ρ∗ ≤ 1, so that λ∗ : = log ρ∗ < 0. A bit of algebra (using the fact a ≥ 0)
shows that ρ∗ < 1 if and only if a+ b+ c > 0. We then note that at the optimal u∗ = D, we have
b = (1− 2D)δ2, whence
a+ b+ c = δ (1− δ) (1 −D) + (1− 2D)δ2 −Dδ(1 − δ)
= (1− 2D) δ > 0
since D < 1
and δ > 0. Hence, the optimal solution is λ∗ : = log ρ∗ < 0, as specified in the lemma
statement.
E Proof of Lemma 6
A straightforward calculation yields that
) = F (δ∗(
; dc);D) = F (
;D) = − (1− h (D))
as claimed. Turning next to the derivatives, we note that by inspection, the solution λ∗(t) defined in
Lemma 5 is twice continuously differentiable as a function of t. Consequently, the function F (t,D)
is twice continuously differentiable in t. Moreover, the function δ∗(w; dc) is twice continuously
differentiable in w. Overall, we conclude that G(w) = F (δ∗(w; dc);D) is twice continuously differ-
entiable in w, and that we can obtain derivatives via chain rule. Computing the first derivative,
we have
) = δ′(
)F ′(δ∗(
; dc);D) = 0
since δ′(w) = −dc (1− 2w)
dc−1, which reduces to zero at w = 1
. Turning to the second derivative,
we have
) = δ′′(
)F ′(δ∗(
; dc);D) +
F ′′(δ∗(
; dc);D) = δ
)F ′(δ∗(
; dc);D).
We again compute δ′′(w) = 2dc (dc − 1)(1 − 2w)
dc−2, which again reduces to zero at w = 1
since
dc ≥ 4 by assumption.
F Regular LDPC codes are sufficient
Consider a regular (dv, d
c) code from the standard Gallager LDPC ensemble. In order to complete
the proof of Theorem 1, we need to show for suitable choices of degree (dv , d
c), the average weight
enumerator of these codes can be suitably bounded, as in equation (34), by a function B that
satisfies the conditions specified in Section 5.5.
It can be shown [17, 22] that for even degrees d′c, the average weight enumerator of the regular
Gallager ensemble, for any block length m, satisfies the bound
logAm(w) = B(w; dv , d
c) + o(1).
The function B in this relation is defined for w ∈ [0, 1
B(w; dv , d
c) := (1− dv)h (w)− (1−RH) + dv inf
(1 + eλ)d
c + (1− eλ)d
, (56)
and by B(w) = B(w − 1
) for w ∈ [1
, 1]. Given that the minimization problem (56) is strictly
convex, a straightforward calculation of the derivative shows the optimum is achieved at λ∗, where
λ∗ ≤ 0 is the unique solution of the equation
(1 + eλ)d
c−1 − (1− eλ)d
(1 + eλ)d
c + (1− eλ)d
= w. (57)
Some numerical computation for RH = 0.5 and different choices (dv, d
c) yields the curves shown
in Fig. 9.
We now show that for suitable choices of degree (dv , d
c), the function B defined in equation (56)
satisfies the four assumptions specified in Section 5.5. First, for even degrees d′c, the function B
0 0.1 0.2 0.3 0.4 0.5
Weight
LDPC weight enumerators
 = 10
Figure 9. Plots of LDPC weight enumerators for codes of rate RH = 0.5, and check degrees
∈ {6, 8, 10}.
is symmetric about w = 1
, so that assumption (A1) holds. Secondly, we have B(w) ≤ RH , and
moreover, for w = 1
, the optimal λ∗(1
) = 0, so that B(1
) = RH , and assumption (A3) is satisfied.
Next, it is known from the work of Gallager [17], and moreover is clear from the plots in Fig. 9,
that LDPC codes with dv > 2 have linear minimum distance, so that assumption (A4) holds.
The final condition to verify is assumption (A2), concerning the differentiability of B. We
summarize this claim in the following:
Lemma 10. The function B is twice continuously differentiable on (0, 1), and in particular we
) = 0, and B′′(
) < 0. (58)
Proof. Note that for each fixed w ∈ (0, 1), the function
f(λ) =
(1 + eλ)d
c + (1− eλ)d
(e−λ + 1)d
c + (e−λ − 1)d
is strictly convex and twice continuously differentiable as a function of λ. Moreover, the function
f∗(w) := infλ≤0 {f(λ)− λw} corresponds to the conjugate dual [21] of f(λ) + I≤0(λ). Since the
optimum is uniquely attained for each w ∈ (0, 1), an application of Danskin’s theorem [4] yields
that f∗ is differentiable with d
f∗(w) = −λ∗(w), where λ∗ is defined by equation (57). Putting
together the pieces, we have B′(w) = (1 − dv)h
′(w) − dvλ
∗(w). Evaluating at w = 1
yields
) = 0− dvλ
∗(0) = 0 as claimed.
We now claim that λ∗(w) is differentiable. Indeed, let us write the defining relation (57) for
λ∗(w) as F (λ,w) = 0 where F (λ,w) := f ′(λ)−w. Note that F is twice continuously differentiable
in both λ and w; moreover, ∂F
exists for all λ ≤ 0 and w, and satisfies ∂F
(λ,w) = f ′′(λ) > 0
by the strict convexity of f . Hence, applying the implicit function theorem [4] yields that λ∗(w)
is differentiable, and moreover that dλ
(w) = 1/f ′′(λ∗(w)). Hence, combined with our earlier
calculation of B′, we conclude that B′′(w) = (1 − dv)h
′′(w) − dv
f ′′(λ(w))
. Our final step is to
compute the second derivative f ′′. In order to do so, it is convenient to define g = log f ′, and
exploit the relation g′f ′ = f ′′. By definition, we have
g(λ) = λ+ log
(1 + eλ)d
c−1 − (1− eλ)d
− log
(1 + eλ)d
c + (1− eλ)d
whence
g′(λ) = 1 + eλ(d′c − 1)
(1 + eλ)d
c−2 + (1− eλ)d
(1 + eλ)d
c−1 − (1− eλ)d
− eλd′c
(1 + eλ)d
c−1 − (1− eλ)d
(1 + eλ)d
c + (1− eλ)d
Evaluating at w = 1
corresponds to λ(0) = 0, so that
f ′′(λ(
)) = f ′(0) g′(0) =
1 + (d′c − 1)
− d′c
Consequently, combining all of the pieces, we have
B′′(w) = (1− dv)h
)− dv
f ′′(λ(1
dv − 1
− 4dv < 0
as claimed.
G Derivatives of L̃
Here we calculate the first and second derivatives of the function L̃ defined in equation (44). The
first derivative takes the form
L̃′(v) = R log
δ′(v; dc)
δ(v; dc)
− (1− p)
δ′(v; dc)
1− δ(v; dc)
where δ′(v; dc) = dc(1 − 2v)
dc−1. Since δ′(1
; dc) = 0, we have L̃
) = 0 as claimed. Second, using
chain rule, we calculate
L̃′′(v) = −R
δ′′(v; dc)δ(v; dc)− [δ
′(v; dc)]
[δ(v; dc)]
− (1− p)
δ′′(v; dc)
1− δ(v; dc)
+ [δ′(v; dc)]
[1− δ(v; dc)]2
and δ′′(v; dc) = −dc (dc − 1) (1 − 2v)
dc−2. Now for dc > 2, we have δ
) = 0, so that L̃′′(1
−4R < 0 as claimed.
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	Introduction
	Previous and ongoing work
	Our contributions
	Background
	Source and channel coding
	Factor graphs and graphical codes
	Weight enumerating functions
	Optimality of bounded degree compound constructions
	Compound construction
	Main results
	Consequences for coding with side information
	Nested code structure
	Source coding with side information
	Problem formulation
	Coding procedure for SCSI
	Channel coding with side information
	Problem formulation
	Coding procedure for CCSI
	Proof of source coding optimality
	Set-up
	Moment analysis
	Second moment analysis
	Bounding the overlap probability
	Finite degrees are sufficient
	Step A
	Step B
	Step C
	Proof of channel coding optimality
	Discussion
	Basic property of LDGM codes
	Bounds on binomial coefficients
	Proof of Lemma ??
	Proof of Lemma ??
	Proof of Lemma ??
	Regular LDPC codes are sufficient
	Derivatives of L"0365L

  We describe and analyze the joint source/channel coding properties of a class
of sparse graphical codes based on compounding a low-density generator matrix
(LDGM) code with a low-density parity check (LDPC) code. Our first pair of
theorems establish that there exist codes from this ensemble, with all degrees
remaining bounded independently of block length, that are simultaneously
optimal as both source and channel codes when encoding and decoding are
performed optimally. More precisely, in the context of lossy compression, we
prove that finite degree constructions can achieve any pair $(R, D)$ on the
rate-distortion curve of the binary symmetric source. In the context of channel
coding, we prove that finite degree codes can achieve any pair $(C, p)$ on the
capacity-noise curve of the binary symmetric channel. Next, we show that our
compound construction has a nested structure that can be exploited to achieve
the Wyner-Ziv bound for source coding with side information (SCSI), as well as
the Gelfand-Pinsker bound for channel coding with side information (CCSI).
Although the current results are based on optimal encoding and decoding, the
proposed graphical codes have sparse structure and high girth that renders them
well-suited to message-passing and other efficient decoding procedures.

Introduction
Over the past decade, codes based on graphical constructions, including turbo codes [3] and low-
density parity check (LDPC) codes [17], have proven extremely successful for channel coding prob-
lems. The sparse graphical nature of these codes makes them very well-suited to decoding using
efficient message-passing algorithms, such as the sum-product and max-product algorithms. The
asymptotic behavior of iterative decoding on graphs with high girth is well-characterized by the
density evolution method [25, 39], which yields a useful design principle for choosing degree dis-
tributions. Overall, suitably designed LDPC codes yield excellent practical performance under
iterative message-passing, frequently very close to Shannon limits [7].
http://arxiv.org/abs/0704.1818v1
However, many other communication problems involve aspects of lossy source coding, either
alone or in conjunction with channel coding, the latter case corresponding to joint source-channel
coding problems. Well-known examples include lossy source coding with side information (one
variant corresponding to the Wyner-Ziv problem [45]), and channel coding with side information
(one variant being the Gelfand-Pinsker problem [19]). The information-theoretic schemes achieving
the optimal rates for coding with side information involve delicate combinations of source and
channel coding. For problems of this nature—in contrast to the case of pure channel coding—the
use of sparse graphical codes and message-passing algorithm is not nearly as well understood. With
this perspective in mind, the focus of this paper is the design and analysis sparse graphical codes
for lossy source coding, as well as joint source/channel coding problems. Our main contribution
is to exhibit classes of graphical codes, with all degrees remaining bounded independently of the
blocklength, that simultaneously achieve the information-theoretic bounds for both source and
channel coding under optimal encoding and decoding.
1.1 Previous and ongoing work
A variety of code architectures have been suggested for lossy compression and related problems in
source/channel coding. One standard approach to lossy compression is via trellis-code quantization
(TCQ) [26]. The advantage of trellis constructions is that exact encoding and decoding can be
performed using the max-product or Viterbi algorithm [24], with complexity that grows linearly
in the trellis length but exponentially in the constraint length. Various researchers have exploited
trellis-based codes both for single-source and distributed compression [6, 23, 37, 46] as well as
information embedding problems [5, 15, 42]. One limitation of trellis-based approaches is the
fact that saturating rate-distortion bounds requires increasing the trellis constraint length [43],
which incurs exponential complexity (even for the max-product or sum-product message-passing
algorithms).
Other researchers have proposed and studied the use of low-density parity check (LDPC) codes
and turbo codes, which have proven extremely successful for channel coding, in application to
various types of compression problems. These techniques have proven particularly successful for
lossless distributed compression, often known as the Slepian-Wolf problem [18, 40]. An attractive
feature is that the source encoding step can be transformed to an equivalent noisy channel de-
coding problem, so that known constructions and iterative algorithms can be leveraged. For lossy
compression, other work [31] shows that it is possible to approach the binary rate-distortion bound
using LDPC-like codes, albeit with degrees that grow logarithmically with the blocklength.
A parallel line of work has studied the use of low-density generator matrix (LDGM) codes, which
correspond to the duals of LDPC codes, for lossy compression problems [30, 44, 9, 35, 34]. Focusing
on binary erasure quantization (a special compression problem dual to binary erasure channel
coding), Martinian and Yedidia [30] proved that LDGM codes combined with modified message-
passing can saturate the associated rate-distortion bound. Various researchers have used techniques
from statistical physics, including the cavity method and replica methods, to provide non-rigorous
analyses of LDGM performance for lossy compression of binary sources [8, 9, 35, 34]. In the limit
of zero-distortion, this analysis has been made rigorous in a sequence of papers [12, 32, 10, 14].
Moreover, our own recent work [28, 27] provides rigorous upper bounds on the effective rate-
distortion function of various classes of LDGM codes. In terms of practical algorithms for lossy
binary compression, researchers have explored variants of the sum-product algorithm [34] or survey
propagation algorithms [8, 44] for quantizing binary sources.
1.2 Our contributions
Classical random coding arguments [11] show that random binary linear codes will achieve both
channel capacity and rate-distortion bounds. The challenge addressed in this paper is the design
and analysis of codes with bounded graphical complexity, meaning that all degrees in a factor graph
representation of the code remain bounded independently of blocklength. Such sparsity is critical
if there is any hope to leverage efficient message-passing algorithms for encoding and decoding.
With this context, the primary contribution of this paper is the analysis of sparse graphical code
ensembles in which a low-density generator matrix (LDGM) code is compounded with a low-density
parity check (LDPC) code (see Fig. 2 for an illustration). Related compound constructions have
been considered in previous work, but focusing exclusively on channel coding [16, 36, 41]. In
contrast, this paper focuses on communication problems in which source coding plays an essential
role, including lossy compression itself as well as joint source/channel coding problems. Indeed, the
source coding analysis of the compound construction requires techniques fundamentally different
from those used in channel coding analysis. We also note that the compound code illustrated
in Fig. 2 can be applied to more general memoryless channels and sources; however, so as to bring
the primary contribution into sharp focus, this paper focuses exclusively on binary sources and/or
binary symmetric channels.
More specifically, our first pair of theorems establish that for any rate R ∈ (0, 1), there exist
codes from compound LDGM/LDPC ensembles with all degrees remaining bounded independently
of the blocklength that achieve both the channel capacity and the rate-distortion bound. To the
best of our knowledge, this is the first demonstration of code families with bounded graphical
complexity that are simultaneously optimal for both source and channel coding. Building on
these results, we demonstrate that codes from our ensemble have a naturally “nested” structure,
in which good channel codes can be partitioned into a collection of good source codes, and vice
versa. By exploiting this nested structure, we prove that codes from our ensembles can achieve
the information-theoretic limits for the binary versions of both the problem of lossy source coding
with side information (SCSI, known as the Wyner-Ziv problem [45]), and channel coding with side
information (CCSI, known as the Gelfand-Pinsker [19] problem). Although these results are based
on optimal encoding and decoding, a code drawn randomly from our ensembles will, with high
probability, have high girth and good expansion, and hence be well-suited to message-passing and
other efficient decoding procedures.
The remainder of this paper is organized as follows. Section 2 contains basic background
material and definitions for source and channel coding, and factor graph representations of binary
linear codes. In Section 3, we define the ensembles of compound codes that are the primary
focus of this paper, and state (without proof) our main results on their source and channel coding
optimality. In Section 4, we leverage these results to show that our compound codes can achieve
the information-theoretic limits for lossy source coding with side information (SCSI), and channel
coding with side information (CCSI). Sections 5 and 6 are devoted to proofs that codes from the
compound ensemble are optimal for lossy source coding (Section 5) and channel coding (Section 6)
respectively. We conclude the paper with a discussion in Section 7. Portions of this work have
previously appeared as conference papers [28, 29, 27].
2 Background
In this section, we provide relevant background material on source and channel coding, binary
linear codes, as well as factor graph representations of such codes.
2.1 Source and channel coding
A binary linear code C of block length n consists of all binary strings x ∈ {0, 1}n satisfying a
set of m < n equations in modulo two arithmetic. More precisely, given a parity check matrix
H ∈ {0, 1}m×n, the code is given by the null space
C : = {x ∈ {0, 1}n | Hx = 0} . (1)
Assuming the parity check matrix H is full rank, the code C consists of 2n−m = 2nR codewords,
where R = 1− m
is the code rate.
Channel coding: In the channel coding problem, the transmitter chooses some codeword x ∈ C
and transmits it over a noisy channel, so that the receiver observes a noise-corrupted version Y .
The channel behavior is modeled by a conditional distribution P(y | x) that specifies, for each
transmitted sequence Y , a probability distribution over possible received sequences {Y = y}. In
many cases, the channel is memoryless, meaning that it acts on each bit of C in an independent
manner, so that the channel model decomposes as P(y | x) =
i=1 fi(xi; yi) Here each function
fi(xi; yi) = P(yi | xi) is simply the conditional probability of observing bit yi given that xi was
transmitted. As a simple example, in the binary symmetric channel (BSC), the channel flips each
transmitted bit xi with probability p, so that P(yi | xi) = (1 − p) I[xi = yi] + p (1− I[xi 6= yi]),
where I(A) represents an indicator function of the event A. With this set-up, the goal of the re-
ceiver is to solve the channel decoding problem: estimate the most likely transmitted codeword,
given by x̂ : = argmax
P(y | x). The Shannon capacity [11] of a channel specifies an upper bound
on the rate R of any code for which transmission can be asymptotically error-free. Continuing
with our example of the BSC with flip probability p, the capacity is given by C = 1− h(p), where
h(p) := −p log2 p− (1− p) log2(1− p) is the binary entropy function.
Lossy source coding: In a lossy source coding problem, the encoder observes some source se-
quence S ∈ S, corresponding to a realization of some random vector with i.i.d. elements Si ∼ PS .
The idea is to compress the source by representing each source sequence S by some codeword
x ∈ C. As a particular example, one might be interested in compressing a symmetric Bernoulli
source, consisting of binary strings S ∈ {0, 1}n, with each element Si drawn in an independent
and identically distributed (i.i.d.) manner from a Bernoulli distribution with parameter p = 1
One could achieve a given compression rate R = m
by mapping each source sequence to some
codeword x ∈ C from a code containing 2m = 2nR elements, say indexed by the binary sequences
z ∈ {0, 1}m. In order to assess the quality of the compression, we define a source decoding map
x 7→ Ŝ(x), which associates a source reconstruction Ŝ(x) with each codeword x ∈ C. Given some
distortion metric d : S × S → R+, the source encoding problem is to find the codeword with min-
imal distortion—namely, the optimal encoding x̂ : = argmin
d(Ŝ(x), S). Classical rate-distortion
theory [11] specifies the optimal trade-offs between the compression rate R and the best achievable
average distortion D = E[d(Ŝ, S)], where the expectation is taken over the random source sequences
S. For instance, to follow up on the Bernoulli compression example, if we use the Hamming metric
d(Ŝ, S) = 1
i=1 |Ŝi − Si| as the distortion measure, then the rate-distortion function takes the
form R(D) = 1− h(D), where h is the previously defined binary entropy function.
We now provide definitions of “good” source and channel codes that are useful for future reference.
Definition 1. (a) A code family is a good D-distortion binary symmetric source code if for any
ǫ > 0, there exists a code with rate R < 1− h (D) + ǫ that achieves Hamming distortion less than
or equal to D.
(b) A code family is a good BSC(p)-noise channel code if for any ǫ > 0 there exists a code with
rate R > 1− h (p)− ǫ with error probability less than ǫ.
2.2 Factor graphs and graphical codes
Both the channel decoding and source encoding problems, if viewed naively, require searching over
an exponentially large codebook (since |C| = 2nR for a code of rate R). Therefore, any practically
useful code must have special structure that facilitates decoding and encoding operations. The
success of a large subclass of modern codes in use today, especially low-density parity check (LDPC)
codes [17, 38], is based on the sparsity of their associated factor graphs.
PSfrag replacements
y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12
c1 c2 c3 c4 c5 c6
PSfrag replacements
x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12
z1 z2 z3 z4 z5 z6 z7 z8 z9
(a) (b)
Figure 1. (a) Factor graph representation of a rate R = 0.5 low-density parity check (LDPC) code
with bit degree dv = 3 and check degree d
= 6. (b) Factor graph representation of a rate R = 0.75
low-density generator matrix (LDGM) code with check degree dc = 3 and bit degree dv = 4.
Given a binary linear code C, specified by parity check matrix H, the code structure can be
captured by a bipartite graph, in which circular nodes (◦) represent the binary values xi (or columns
of H), and square nodes (�) represent the parity checks (or rows of H). For instance, Fig. 1(a)
shows the factor graph for a rate R = 1
code in parity check form, with m = 6 checks acting on
n = 12 bits. The edges in this graph correspond to 1’s in the parity check matrix, and reveal the
subset of bits on which each parity check acts. The parity check code in Fig. 1(a) is a regular
code with bit degree 3 and check degree 6. Such low-density constructions, meaning that both the
bit degrees and check degrees remain bounded independently of the block length n, are of most
practical use, since they can be efficiently represented and stored, and yield excellent performance
under message-passing decoding. In the context of a channel coding problem, the shaded circular
nodes at the top of the low-density parity check (LDPC) code in panel (a) represent the observed
variables yi received from the noisy channel.
Figure 1(b) shows a binary linear code represented in factor graph form by its generator matrix
G. In this dual representation, each codeword x ∈ {0, 1}n is generated by taking the matrix-vector
product of the form Gz, where z ∈ {0, 1}m is a sequence of information bits, and G ∈ {0, 1}n×m is
the generator matrix. For the code shown in panel (b), the blocklength is n = 12, and information
sequences are of length m = 9, for an overall rate of R = m/n = 0.75 in this case. The degrees of
the check and variable nodes in the factor graph are dc = 3 and dv = 4 respectively, so that the
associated generator matrix G has dc = 3 ones in each row, and dv = 4 ones in each column. When
the generator matrix is sparse in this setting, then the resulting code is known as a low-density
generator matrix (LDGM) code.
2.3 Weight enumerating functions
For future reference, it is useful to define the weight enumerating function of a code. Given a binary
linear code of blocklength m, its codewords x have renormalized Hamming weights w : =
range in the interval [0, 1]. Accordingly, it is convenient to define a function Wm : [0, 1] → R+ that,
for each w ∈ [0, 1], counts the number of codewords of weight w:
Wm(w) :=
x ∈ C | w =
⌉ }∣∣∣∣ , (2)
where ⌈·⌉ denotes the ceiling function. Although it is typically difficult to compute the weight enu-
merator itself, it is frequently possible to compute (or bound) the average weight enumerator, where
the expectation is taken over some random ensemble of codes. In particular, our analysis in the
sequel makes use of the average weight enumerator of a (dv, d
c)-regular LDPC code (see Fig. 1(a)),
defined as
Am(w; dv , d
c) :=
logE [Wm(w)] , (3)
where the expectation is taken over the ensemble of all regular (dv , d
c)-LDPC codes. For such
regular LDPC codes, this average weight enumerator has been extensively studied in previous
work [17, 22].
3 Optimality of bounded degree compound constructions
In this section, we describe the compound LDGM/LDPC construction that is the focus of this
paper, and describe our main results on their source and channel coding optimality.
3.1 Compound construction
Our main focus is the construction illustrated in Fig. 2, obtained by compounding an LDGM code
(top two layers) with an LDPC code (bottom two layers). The code is defined by a factor graph
with three layers: at the top, a vector x ∈ {0, 1}n of codeword bits is connected to a set of n parity
checks, which are in turn connected by a sparse generator matrix G to a vector y ∈ {0, 1}m of
information bits in the middle layer. The information bits y are also codewords in an LDPC code,
defined by the parity check matrix H connecting the middle and bottom layers.
In more detail, considering first the LDGM component of the compound code, each codeword
x ∈ {0, 1}n in the top layer is connected via the generator matrix G ∈ {0, 1}n×m to an information
sequence y ∈ {0, 1}m in the middle layer; more specifically, we have the algebraic relation x = Gy.
Note that this LDGM code has rate RG ≤
. Second, turning to the LDPC component of the
compound construction, its codewords correspond to a subset of information sequences y ∈ {0, 1}m
in the middle layer. In particular, any valid codeword y satisfies the parity check relation Hy = 0,
where H ∈ {0, 1}m×k joins the middle and bottom layers of the construction. Overall, this defines
an LDPC code with rate RH = 1−
, assuming that H has full row rank.
The overall code C obtained by concatenating the LDGM and LDPC codes has blocklength n,
and rate R upper bounded by RGRH . In algebraic terms, the code C is defined as
C : = {x ∈ {0, 1}n | x = Gy for some y ∈ {0, 1}m such that Hy = 0} , (4)
where all operations are in modulo two arithmetic.
PSfrag replacements
k1 k2
H1 H2
Figure 2. The compound LDGM/LDPC construction analyzed in this paper, consisting of a (n,m)
LDGM code over the middle and top layers, compounded with a (m, k) LDPC code over the middle
and bottom layers. Codewords x ∈ {0, 1}n are placed on the top row of the construction, and are
associated with information bit sequences z ∈ {0, 1}m in the middle layer. The LDGM code over the
top and middle layers is defined by a sparse generator matrix G ∈ {0, 1}n×m with at most dc ones
per row. The bottom LDPC over the middle and bottom layers is represented by a sparse parity
check matrix H ∈ {0, 1}k×m with dv ones per column, and d
ones per row.
Our analysis in this paper will be performed over random ensembles of compound LDGM/LDPC
ensembles. In particular, for each degree triplet (dc, dv, d
c), we focus on the following random
ensemble:
(a) For each fixed integer dc ≥ 4, the random generator matrix G ∈ {0, 1}
n×m is specified as
follows: for each of the n rows, we choose dc positions with replacement, and put a 1 in each
of these positions. This procedure yields a random matrix with at most dc ones per row,
since it is possible (although of asymptotically negligible probability for any fixed dc) that
the same position is chosen more than once.
(b) For each fixed degree pair (dv, d
c), the random LDPC matrix H ∈ {0, 1}
k×m is chosen uni-
formly at random from the space of all matrices with exactly dv ones per column, and exactly
d′c ones per row. This ensemble is a standard (dv , d
c)-regular LDPC ensemble.
We note that our reason for choosing the check-regular LDGM ensemble specified in step (a) is not
that it need define a particularly good code, but rather that it is convenient for theoretical purposes.
Interestingly, our analysis shows that the bounded degree dc check-regular LDGM ensemble, even
though it is sub-optimal for both source and channel coding in isolation [28, 29], defines optimal
source and channel codes when combined with a bottom LDPC code.
3.2 Main results
Our first main result is on the achievability of the Shannon rate-distortion bound using codes from
LDGM/LDPC compound construction with finite degrees (dc, dv , d
c). In particular, we make the
following claim:
Theorem 1. Given any pair (R,D) satisfying the Shannon bound, there is a set of finite degrees
(dc, dv , d
c) and a code from the associated LDGM/LDPC ensemble with rate R that is a D-good
source code (see Definition 1).
In other work [28, 27], we showed that standard LDGM codes from the check-regular ensemble
cannot achieve the rate-distortion bound with finite degrees. As will be highlighted by the proof of
Theorem 1 in Section 5, the inclusion of the LDPC lower code in the compound construction plays
a vital role in the achievability of the Shannon rate-distortion curve.
Our second main result of this result is complementary in nature to Theorem 1, regarding the
achievability of the Shannon channel capacity using codes from LDGM/LDPC compound construc-
tion with finite degrees (dc, dv , d
c). In particular, we have:
Theorem 2. For all rate-noise pairs (R, p) satisfying the Shannon channel coding bound R <
1 − h (p), there is a set of finite degrees (dc, dv , d
c) and a code from the associated LDGM/LDPC
ensemble with rate R that is a p-good channel code (see Definition 1).
To put this result into perspective, recall that the overall rate of this compound construction is
given by R = RGRH . Note that an LDGM code on its own (i.e., without the lower LDPC code)
is a special case of this construction with RH = 1. However, a standard LDGM of this variety is
not a good channel code, due to the large number of low-weight codewords. Essentially, the proof
of Theorem 2 (see Section 6) shows that using a non-trivial LDPC lower code (with RH < 1) can
eliminate these troublesome low-weight codewords.
4 Consequences for coding with side information
We now turn to consideration of the consequences of our two main results for problems of coding
with side information. It is well-known from previous work [47] that achieving the information-
theoretic limits for these problems requires nested constructions, in which a collection of good source
codes are nested inside a good channel code (or vice versa). Accordingly, we begin in Section 4.1
by describing how our compound construction naturally generates such nested ensembles. In Sec-
tions 4.2 and 4.3 respectively, we discuss how the compound construction can be used to achieve
the information-theoretic optimum for binary source coding with side information (a version of the
Wyner-Ziv problem [45]), and binary information embedding (a version of “dirty paper coding”,
or the Gelfand-Pinsker problem [19]).
4.1 Nested code structure
The structure of the compound LDGM/LDPC construction lends itself naturally to nested code
constructions. In particular, we first partition the set of k lower parity checks into two disjoint
subsets K1 and K2, of sizes k1 and k2 respectively, as illustrated in Fig. 2. Let H1 and H2 denote
the corresponding partitions of the full parity check matrix H ∈ {0, 1}k×m. Now let us set all parity
bits in the subset K2 equal to zero, and consider the LDGM/LDPC code C(G,H1) defined by the
generator matrix G and the parity check (sub)matrix H1, as follows
C(G,H1) := {x ∈ {0, 1}
n | x = Gy for some y ∈ {0, 1}m such that H1 y = 0} . (5)
Note that the rate of C(G,H1) is given by R
′ = RGRH1 , which can be suitably adjusted by
modifying the LDGM and LDPC rates respectively. Moreover, by applying Theorems 1 and 2,
there exist finite choices of degree such that C(G,H1) will be optimal for both source and channel
coding.
Considering now the remaining k2 parity bits in the subset K2, suppose that we set them equal
to a fixed binary sequence m ∈ {0, 1}k2 . Now consider the code
C(m) :=
x ∈ {0, 1}n | x = Gy for some y ∈ {0, 1}m such that
. (6)
Note that for each binary sequencem ∈ {0, 1}k2 , the code C(m) is a subcode of C(G,H1); moreover,
the collection of these subcodes forms a disjoint partition as follows
C(G,H1) =
m∈{0,1}k2
C(m). (7)
Again, Theorems 1 and 2 guarantee that (with suitable degree choices), each of the subcodes C(m)
is optimal for both source and channel coding. Thus, the LDGM/LDPC construction has a natural
nested property, in which a good source/channel code—namely C(G,H1)—is partitioned into a
disjoint collection {C(m), m ∈ {0, 1}k1} of good source/channel codes. We now illustrate how this
nested structure can be exploited for coding with side information.
4.2 Source coding with side information
We begin by showing that the compound construction can be used to perform source coding with
side information (SCSI).
4.2.1 Problem formulation
Suppose that we wish to compress a symmetric Bernoulli source S ∼ Ber(1
) so as to be able
to reconstruct it with Hamming distortion D. As discussed earlier in Section 2, the minimum
achievable rate is given by R(D) = 1− h (D). In the Wyner-Ziv extension of standard lossy com-
pression [45], there is an additional source of side information about S—say in the form Z = S ⊕W
where W ∼ Ber(δ) is observation noise—that is available only at the decoder. See Fig. 3 for a block
diagram representation of this problem.
Decoder
PSfrag replacements
Figure 3. Block diagram representation of source coding with side information (SCSI). A source S is
compressed to rate R. The decoder is given the compressed version, and side information Z = S⊕W ,
and wishes to use (Ŝ, Z) to reconstruct the source S up to distortion D.
For this binary version of source coding with side information (SCSI), it is known [2] that the
minimum achievable rate takes the form
RWZ(D, p) = l. c. e.
h (D ∗ p)− h (D) , (p, 0)
, (8)
where l. c. e. denotes the lower convex envelope. Note that in the special case p = 1
, the side
information is useless, so that the Wyner-Ziv rate reduces to classical rate-distortion. In the
discussion to follow, we focus only on achieving rates of the form h (D ∗ p)−h (D), as any remaining
rates on the Wyner-Ziv curve (8) can be achieved by time-sharing with the point (p, 0).
4.2.2 Coding procedure for SCSI
In order to achieve rates of the form R = h (D ∗ p)− h (D), we use the compound LDGM/LDPC
construction, as illustrated in Fig. 2, according to the following procedure.
Step #1, Source coding: The first step is a source coding operation, in which we transform the
source sequence S to a quantized representation S. In order to do so, we use the code C(G,H1), as
defined in equation (5) and illustrated in Fig. 4(a), composed of the generator matrix G and the
parity check matrix H1. Note that C(G,H1), when viewed as a code with blocklength n, has rate
R1 : =
1− k1
= m−k1
. Suppose that we choose1 the middle and lower layer sizes m and k1
respectively such that
m− k1
= 1− h (D) + ǫ/2, (9)
where ǫ > 0 is arbitrary. For any such choice, Theorem 1 guarantees the existence of finite degrees
(dc, dv , d
c) such that that C(G,H1) is a good D-distortion source code. Consequently, for the speci-
fied rate R1, we can use C(G,H1) in order to transform the source to some quantized representation
Ŝ such that the error Ŝ ⊕ S has average Hamming weighted bounded by D. Moreover, since Ŝ is a
codeword of C(G,H1), there is some sequence of information bits Ŷ ∈ {0, 1}
m such that Ŝ = GŶ
and H1Ŷ = 0.
PSfrag replacements
0 0 0
PSfrag replacements
0 0 0 1 0
k1 k2
H1 H2
(a) (b)
Figure 4. (a) Source coding stage for Wyner-Ziv procedure: the C(G,H1), specified by the generator
matrix G ∈ {0, 1}n×m and parity check matrix H1 ∈ {0, 1}
k1×m, is used to quantize the source vector
S ∈ {0, 1}n, thereby obtaining a quantized version Ŝ ∈ {0, 1}n and associated vector of information
bits Ŷ ∈ {0, 1}m, such that Ŝ = G Ŷ and H1 Ŷ = 0.
Step #2. Channel coding: Given the output (Ŷ , Ŝ) of the source coding step, consider the
sequence H2Ŷ ∈ {0, 1}
k2 of parity bits associated with the parity check matrix H2. Transmitting
this string of parity bits requires rate Rtrans =
. Overall, the decoder receives both these k2 parity
bits, as well as the side information sequence Z = S ⊕W . Using these two pieces of information,
the goal of the decoder is to recover the quantized sequence Ŝ.
Viewing this problem as one of channel coding, the effective rate of this channel code is
m−k1−k2
. Note that the side information can be written in the form
Z = S ⊕W = Ŝ ⊕ E ⊕W,
1Note that the choices of m and k1 need not be unique.
where E : = S⊕Ŝ is the quantization noise, andW ∼ Ber(p) is the channel noise. If the quantization
noise E were i.i.d. Ber(D), then the overall effective noise E ⊕W would be i.i.d. Ber(D ∗ p). (In
reality, the quantization noise is not exactly i.i.d. Ber(D), but it can be shown [47] that it can be
treated as such for theoretical purposes.) Consequently, if we choose k2 such that
m− k1 − k2
= 1− h (D ∗ p)− ǫ/2, (10)
for an arbitrary ǫ > 0, then Theorem 2 guarantees that the decoder will (w.h.p.) be able to recover
a codeword corrupted by (D ∗ p)-Bernoulli noise.
Summarizing our findings, we state the following:
Corollary 1. There exist finite choices of degrees (dc, dv, d
c) such that the compound LDGM/LDPC
construction achieves the Wyner-Ziv bound.
Proof. With the source coding rate R1 chosen according to equation (9), the encoder will return a
quantization Ŝ with average Hamming distance to the source S of at most D. With the channel
coding rate R2 chosen according to equation (10), the decoder can with high probability recover
the quantization Ŝ. The overall transmission rate of the scheme is
Rtrans =
m− k1
m− k1 − k2
= R1 − R2
= (1− h (D) + ǫ/2)− (1− h (D ∗ p)− ǫ/2)
= h (D ∗ p)− h (D) + ǫ.
Since ǫ > 0 was arbitrary, we have established that the scheme can achieve rates arbitrarily close
to the Wyner-Ziv bound.
4.3 Channel coding with side information
We now show how the compound construction can be used to perform channel coding with side
information (CCSI).
4.3.1 Problem formulation
In the binary information embedding problem, given a specified input vector V ∈ {0, 1}n, the
channel output Z ∈ {0, 1}n is assumed to take the form
Z = V ⊕ S ⊕W, (11)
where S is a host signal (not under control of the user), and W ∼ Ber(p) corresponds to channel
noise. The encoder is free to choose the input vector V ∈ {0, 1}n, subject to an average channel
constraint
E [‖V ‖1] ≤ w, (12)
for some parameter w ∈ (0, 1
]. The goal is to use a channel coding scheme that satisfies this
constraint (12) so as to maximize the number of possible messages m that can be reliably commu-
nicated. Moreover, We write V ≡ V
to indicate that each channel input is implicitly identified
with some underlying message m. Given the channel output Z = V
⊕ S ⊕W , the goal of the de-
coder is to recover the embedded message m. The capacity for this binary information embedding
problem [2] is given by
RIE(w, p) = u. c. e.
h (w)− h (p) , (0, 0)
, (13)
where u. c. e. denotes the upper convex envelope. As before, we focus on achieving rates of the form
h (w) − h (p), since any remaining points on the curve (13) can be achieved via time-sharing with
the (0, 0) point.
Encoder Decoder
PSfrag replacements
Figure 5. Block diagram representation of channel coding with side information (CCSI). The
encoder embeds a message m into the channel input V
, which is required to satisfy the average
channel constraint 1
‖1] ≤ w. The channel produces the output Z = Vm⊕S⊕W , where S is a
host signal known only to the encoder, and W ∼ Ber(p) is channel noise. Given the channel output
Y , the decoder outputs an estimate m̂ of the embedded message.
4.3.2 Coding procedure for CCSI
In order to achieve rates of the form R = h (w)− h (p), we again use the compound LDGM/LDPC
construction in Fig. 2, now according to the following two step procedure.
Step #1: Source coding: The goal of the first stage is to embed the message into the transmitted
signal V via a quantization process. In order to do so, we use the code illustrated in Fig. 6(a),
specified by the generator matrix G and parity check matrices H1 and H2. The set K1 of k1 parity
bits associated with the check matrix H1 remain fixed to zero throughout the scheme. On the other
hand, we use the remaining k2 lower parity bits associated with H2 to specify a particular message
m ∈ {0, 1}k2 that the decoder would like to recover. In algebraic terms, the resulting code C(m)
has the form
C(m) :=
x ∈ {0, 1}n | x = Gy for some y ∈ {0, 1}m such that
.(14)
Since the encoder has access to host signal S, it may use this code C(m) in order to quantize the
host signal. After doing so, the encoder has a quantized signal Ŝ
∈ {0, 1}n and an associated
sequence Ŷ
∈ {0, 1}m of information bits such that Ŝ
= GŶ
. Note that the quantized signal
) specifies the message m in an implicit manner, since m = H2 Ŷm by construction of the
code C(m).
Now suppose that we choose n,m and k such that
m− k1 − k2
= 1− h (w) + ǫ/2 (15)
for some ǫ > 0, then Theorem 1 guarantees that there exist finite degrees (dc, dv , d
c) such that
the resulting code is a good w-distortion source code. Otherwise stated, we are guaranteed that
w.h.p, the quantization error E : = S ⊕ Ŝ has average Hamming weight upper bounded by wn.
Consequently, we may set the channel input V equal to the quantization noise (V = E), thereby
ensuring that the average channel constraint (12) is satisfied.
Step #2, Channel coding: In the second phase, the decoder is given a noisy channel observation
of the form
Z = E ⊕ S ⊕W = Ŝ ⊕W, (16)
and its task is to recover Ŝ. In terms of the code architecture, the k1 lower parity bits remain set to
zero; the remaining k2 parity bits, which represent the message m, are unknown to the coder. The
resulting code, as illustrated illustrated in Fig. 6(b), can be viewed as channel code with effective
PSfrag replacements
0 0 0 1 0
H1 H2
PSfrag replacements
0 0 0
(a) (b)
Figure 6. (a) Source coding step for binary information embedding. The message m ∈ {0, 1}k2
specifies a particular coset; using this particular source code, the host signal S is compressed to Ŝ,
and the quantization error E = S ⊕ Ŝ is transmitted over the constrained channel. (b) Channel
coding step for binary information embedding. The decoder receives Z = Ŝ ⊕W where W ∼ Ber(p)
is channel noise, and seeks to recover Ŝ, and hence the embedded message m specifying the coset.
rate m−k1
. Now suppose that we choose k1 such that the effective code used by the decoder has
m− k1
= 1− h (p)− ǫ/2, (17)
for some ǫ > 0. Since the channel noise W is Ber(p) and the rate R2 chosen according to (17),
Theorem 2 guarantees that the decoder will w.h.p. be able to recover the pair Ŝ and Ŷ . Moreover,
by design of the quantization procedure, we have the equivalence m = H2 Ŷ so that a simple
syndrome-forming procedure allows the decoder to recover the hidden message.
Summarizing our findings, we state the following:
Corollary 2. There exist finite choices of degrees (dc, dv, d
c) such that the compound LDGM/LDPC
construction achieves the binary information embedding (Gelfand-Pinsker) bound.
Proof. With the source coding rate R1 chosen according to equation (15), the encoder will return
a quantization Ŝ of the host signal S with average Hamming distortion upper bounded by w.
Consequently, transmitting the quantization error E = S ⊕ Ŝ will satisfy the average channel
constraint (12). With the channel coding rate R2 chosen according to equation (17), the decoder
can with high probability recover the quantized signal Ŝ, and hence the message m. Overall, the
scheme allows a total of 2k2 distinct messages to be embedded, so that the effective information
embedding rate is
Rtrans =
m− k1
m− k1 − k2
= R2 − R1
= (1− h (p)− ǫ/2) − (1− h (w) + ǫ/2)
= h (w)− h (p) + ǫ,
for some ǫ > 0. Thus, we have shown that the proposed scheme achieves the binary information
embedding bound (13).
5 Proof of source coding optimality
This section is devoted to the proof of the previously stated Theorem 1 on the source coding
optimality of the compound construction.
5.1 Set-up
In establishing a rate-distortion result such as Theorem 1, perhaps the most natural focus is the
random variable
dn(S,C) :=
‖x− S‖1, (18)
corresponding to the (renormalized) minimum Hamming distance from a random source sequence
S ∈ {0, 1}n to the nearest codeword in the code C. Rather than analyzing this random variable
directly, our proof of Theorem 1 proceeds indirectly, by studying an alternative random variable.
Given a binary linear code with N codewords, let i = 0, 1, 2, . . . , N − 1 be indices for the
different codewords. We say that a codeword Xi is distortion D-good for a source sequence S if the
Hamming distance ‖Xi⊕S‖1 is at most Dn. We then set the indicator random variable Z
i(D) = 1
when codeword Xi is distortion D-good. With these definitions, our proof is based on the following
random variable:
Tn(S,C;D) :=
Zi(D). (19)
Note that Tn(S,C;D) simply counts the number of codewords that are distortion D-good for a
source sequence S. Moreover, for all distortions D, the random variable Tn(S,C;D) is linked to
dn(S,C) via the equivalence
P[Tn(S,C;D) > 0] = P[dn(S,C) ≤ D]. (20)
Throughout our analysis of P[Tn(S,C;D) > 0], we carefully track only its exponential behavior.
More precisely, the analysis to follow will establish an inverse polynomial lower bound of the
form P[Tn(S,C;D) > 0] ≥ 1/f(n) where f(·) collects various polynomial factors. The following
concentration result establishes that the polynomial factors in these bounds can be ignored:
Lemma 1 (Sharp concentration). Suppose that for some target distortion D, we have
P[Tn(S,C;D) > 0] ≥ 1/f(n), (21)
where f(·) is a polynomial function satisfying log f(n) = o(n). Then for all ǫ > 0, there exists a
fixed code C̄ of sufficiently large blocklength n such that E[dn(S; C̄)] ≤ D + ǫ.
Proof. Let us denote the random code C as (C1,C2), where C1 denotes the random LDGM top
code, and C2 denotes the random LDPC bottom code. Throughout the analysis, we condition on
some fixed LDPC bottom code, say C2 = C̄2. We begin by showing that the random variable
(dn(S,C) | C̄2) is sharply concentrated. In order to do so, we construct a vertex-exposure martin-
gale [33] of the following form. Consider a fixed sequential labelling {1, . . . , n} of the top LDGM
checks, with check i associated with source bit Si. We reveal the check and associated source
bit in a sequential manner for each i = 1, . . . , n, and so define a sequence of random variables
{U0, U1, . . . , Un} via U0 : = E[dn(S,C) | C̄2], and
Ui : = E
dn(S,C) | S1, . . . , Si, C̄2
, i = 1, . . . , n. (22)
By construction, we have Un = (dn(S,C) | C̄2). Moreover, this sequence satisfies the following
bounded difference property: adding any source bit Si and the associated check in moving from
Ui−1 to Ui can lead to a (renormalized) change in the minimum distortion of at most ci = 1/n.
Consequently, by applying Azuma’s inequality [1], we have, for any ǫ > 0,
[∣∣(dn(S,C) | C̄2)− E[dn(S,C) | C̄2]
∣∣ ≥ ǫ
≤ exp
. (23)
Next we observe that our assumption (21) of inverse polynomial decay implies that, for at least
one bottom code C̄2,
P[dn(S,C) ≤ D | C̄2] = P[Tn(S,C;D) > 0 | C̄2] ≥ 1/g(n), (24)
for some subexponential function g. Otherwise, there would exist some α > 0 such that
P[Tn(S,C;D) > 0 | C̄2] ≤ exp(−nα)
for all choices of bottom code C̄2, and taking averages would violate our assumption (21).
Finally, we claim that the concentration result (23) and inverse polynomial bound (24) yield
the result. Indeed, if for some ǫ > 0, we had D < E[dn(S,C) | C̄2] − ǫ, then the concentration
bound (23) would imply that the probability
P[dn(S,C) ≤ D | C̄2] ≤ P[dn(S,C) ≤ E[dn(S,C) | C̄2]− ǫ | C̄2]
[∣∣(dn(S,C) | C̄2)− E[dn(S,C) | C̄2]
∣∣ ≥ ǫ
decays exponentially, which would contradict the inverse polynomial bound (24) for sufficiently
large n. Thus, we have shown that assumption (21) implies that for all ǫ > 0, there exists a
sufficiently large n and fixed bottom code C̄2 such that E[dn(S,C) | C̄2] ≤ D + ǫ. If the average
over LDGM codes C1 satisfies this bound, then at least one choice of LDGM top code must also
satisfy it, whence we have established that there exists a fixed code C̄ such that E[dn(S; C̄)] ≤ D+ǫ,
as claimed.
5.2 Moment analysis
In order to analyze the probability P[Tn(S,C;D) > 0], we make use of the moment bounds given
in the following elementary lemma:
Lemma 2 (Moment methods). Given any random variable N taking non-negative integer values,
there holds
(E[N ])
E[N2]
≤ P[N > 0]
≤ E[N ]. (25)
Proof. The upper bound (b) is an immediate consequence of Markov’s inequality, whereas the lower
bound (a) follows by applying the Cauchy-Schwarz inequality [20] as follows
(E[N ])
N I[N > 0]
≤ E[N2] E
2[N > 0]
= E[N2] P[N > 0].
The remainder of the proof consists in applying these moment bounds to the random variable
Tn(S,C;D), in order to bound the probability P[Tn(S,C;D) > 0]. We begin by computing the first
moment:
Lemma 3 (First moment). For any code with rate R, the expected number of D-good codewords
scales exponentially as
logE[Tn] = [R− (1− h (D))] ± o(1). (26)
Proof. First, by linearity of expectation E[Tn] =
∑2nR−1
i=0 P[Z
i(D) = 1] = 2nRP[Z0(D) = 1], where
we have used symmetry of the code construction to assert that P[Zi(D) = 1] = P[Z0(D) = 1] for
all indices i. Now the event {Z0(D) = 1} is equivalent to an i.i.d Bernoulli(1
) sequence of length
n having Hamming weight less than or equal to Dn. By standard large deviations theory (either
Sanov’s theorem [11], or direct asymptotics of binomial coefficients), we have
logP[Z0(D) = 1] = 1− h (D) ± o(1),
which establishes the claim.
Unfortunately, however, the first moment E[Tn] need not be representative of typical behavior
of the random variable Tn, and hence overall distortion performance of the code. As a simple
illustration, consider an imaginary code consisting of 2nR copies of the all-zeroes codeword. Even
for this “code”, as long as R > 1− h (D), the expected number of distortion-D optimal codewords
grows exponentially. Indeed, although Tn = 0 for almost all source sequences, for a small subset of
source sequences (of probability mass ≈ 2−n [1−h(D)]), the random variable Tn takes on the enormous
value 2nR, so that the first moment grows exponentially. However, the average distortion incurred
by using this code will be ≈ 0.5 for any rate, so that the first moment is entirely misleading. In order
to assess the representativeness of the first moment, one needs to ensure that it is of essentially the
same order as the variance, hence the comparison involved in the second moment bound (25)(a).
5.3 Second moment analysis
Our analysis of the second moment begins with the following alternative representation:
Lemma 4.
E[T 2n(D)] = E[Tn(D)]
j 6=0
P[Zj(D) = 1 | Z0(D) = 1]
. (27)
Based on this lemma, proved in Appendix C, we see that the key quantity to control is the condi-
tional probability P[Zj(D) = 1 | Z0(D) = 1]. It is this overlap probability that differentiates the
low-density codes of interest here from the unstructured codebooks used in classical random coding
arguments.2 For a low-density graphical code, the dependence between the events {Zj(D) = 1}
and {Z0(D) = 1} requires some analysis.
Before proceeding with this analysis, we require some definitions. Recall our earlier definition (3)
of the average weight enumerator associated with an (dv , d
c) LDPC code, denoted by Am(w).
Moreover, let us define for each w ∈ [0, 1] the probability
Q(w;D) := P [‖X(w) ⊕ S‖1 ≤ Dn | ‖S‖1 ≤ Dn] , (28)
where the quantity X(w) ∈ {0, 1}n denotes a randomly chosen codeword, conditioned on its under-
lying length-m information sequence having Hamming weight ⌈wm⌉. As shown in Lemma 9 (see
Appendix A), the random codeword X(w) has i.i.d. Bernoulli elements with parameter
δ∗(w; dc) =
1− (1− 2w)dc
. (29)
With these definitions, we now break the sum on the RHS of equation (27) intom terms, indexed
by t = 1, 2, . . . ,m, where term t represents the contribution of a given non-zero information sequence
y ∈ {0, 1}m with (Hamming) weight t. Doing so yields
j 6=0
P[Zj(D) = 1 | Z0(D) = 1] =
Am(t/m)Q(t/m;D)
≤ m max
1≤t≤m
{Am(t/m) Q(t/m;D)}
≤ m max
w∈[0,1]
{Am(w) Q(w;D)} .
Consequently, we need to control both the LDPC weight enumerator Am(w) and the probability
Q(w;D) over the range of possible fractional weights w ∈ [0, 1].
5.4 Bounding the overlap probability
The following lemma, proved in Appendix D, provides a large deviations bound on the probability
Q(w;D).
Lemma 5. For each w ∈ [0, 1], we have
logQ(w;D) ≤ F (δ∗(w; dc);D) + o(1), (30)
2In the latter case, codewords are chosen independently from some ensemble, so that the overlap probability is
simply equal to P[Zj(D) = 1]. Thus, for the simple case of unstructured random coding, the second moment bound
actually provides the converse to Shannon’s rate-distortion theorem for the symmetric Bernoulli source.
where for each t ∈ (0, 1
] and D ∈ (0, 1
], the error exponent is given by
F (t;D) := D log
(1− t)eλ
+ (1−D) log
(1− t) + teλ
− λ∗D. (31)
Here λ∗ : = log
b2−4ac
, where a : = t (1− t) (1−D), b : = (1− 2D)t2, and c : = −t (1− t)D.
In general, for any D ∈ (0, 1
], the function F ( · ;D) has the following properties. At t = 0,
it achieves its maximum F (0 ;D) = 0, and then is strictly decreasing on the interval (0, 1
], ap-
proaching its minimum value − [1− h (D)] as t → 1
. Figure 7 illustrates the form of the function
F (δ∗(ω; dc);D) for two different values of distortion D, and for degrees dc ∈ {3, 4, 5}. Note that
0 0.1 0.2 0.3 0.4 0.5
Weight
Decay of overlap probability: D = 0.1101
0 0.1 0.2 0.3 0.4 0.5
Weight
Decay of overlap probability: D = 0.3160
(a) (b)
Figure 7. Plot of the upper bound (30) on the overlap probability 1
logQ(w;D) for different choices
of the degree dc, and distortion probabilities. (a) Distortion D = 0.1100. (b) Distortion D = 0.3160.
increasing dc causes F (δ
∗(ω; dc);D) to approach its minimum −[1− h (D)] more rapidly.
We are now equipped to establish the form of the effective rate-distortion function for any
compound LDGM/LDPC ensemble. Substituting the alternative form of E[T 2n ] from equation (27)
into the second moment lower bound (25) yields
logP[Tn(D) > 0] ≥
logE[Tn(D)]− log
j 6=0
P[Zj(D) = 1 | Z0(D) = 1]
≥ R− (1− h (D))− max
w∈[0,1]
logAm(w) +
logQ(w;D)
− o(1)
≥ R− (1− h (D))− max
w∈[0,1]
logAm(w)
+ F ( δ∗(w; dc),D)
− o(1), (32)
0 0.1 0.2 0.3 0.4 0.5
Weight
Minimum achievable rates: (R,D) = (0.50, 0.1100)
Compound
Naive LDGM
0 0.1 0.2 0.3 0.4 0.5
0.025
0.075
0.125
Weight
Minimum achievable rates: (R,D) = (0.10, 0.3160)
Compound
Naive LDGM
(a) (b)
Figure 8. Plot of the function defining the lower bound (33) on the minimum achievable rate for a
specified distortion. Shown are curves with LDGM top degree dc = 4, comparing the uncoded case
(no bottom code, dotted curve) to a bottom (4, 6) LDPC code (solid line). (a) Distortion D = 0.1100.
(b) Distortion D = 0.3160.
where the last step follows by applying the upper bound on Q from Lemma 5, and the relation
m = RGn =
n. Now letting B(w; dv , d
c) be any upper bound on the log of average weight
enumerator
logAm(w)
, we can then conclude that 1
log P[Tn(D) > 0] is asymptotically non-negative
for all rate-distortion pairs (R,D) satisfying
R ≥ max
w∈[0,1]
1− h (D) + F (δ∗(w; dc),D)
B(w;dv,d′c)
. (33)
Figure 8 illustrates the behavior of the RHS of equation (33), whose maximum defines the effective
rate-distortion function, for the case of LDGM top degree dc = 4. Panels (a) and (b) show the
cases of distortion D = 0.1100 and D = 0.3160 respectively, for which the respective Shannon rates
are R = 0.50 and R = 0.10. Each panel shows two plots, one corresponding the case of uncoded
information bits (a naive LDGM code), and the other to using a rate RH = 2/3 LDPC code with
degrees (dv , dc) = (4, 6). In all cases, the minimum achievable rate for the given distortion is
obtained by taking the maximum for w ∈ [0, 0.5] of the plotted function. For any choices of D, the
plotted curve is equal to the Shannon bound RSha = 1 − h (D) at w = 0, and decreases to 0 for
w = 1
Note the dramatic difference between the uncoded and compound constructions (LDPC-coded).
In particular, for both settings of the distortion (D = 0.1100 and D = 0.3160), the uncoded curves
rise from their initial values to maxima above the Shannon limit (dotted horizontal line). Con-
sequently, the minimum required rate using these constructions lies strictly above the Shannon
optimum. The compound construction curves, in contrast, decrease monotonically from their max-
imum value, achieved at w = 0 and corresponding to the Shannon optimum. In the following
section, we provide an analytical proof of the fact that for any distortion D ∈ [0, 1
), it is al-
ways possible to choose finite degrees such that the compound construction achieves the Shannon
optimum.
5.5 Finite degrees are sufficient
In order to complete the proof of Theorem 1, we need to show that for all rate-distortion pairs
(R,D) satisfying the Shannon bound, there exist LDPC codes with finite degrees (dv , d
c) and a
suitably large but finite top degree dc such that the compound LDGM/LDPC construction achieves
the specified (R,D).
Our proof proceeds as follows. Recall that in moving from equation (32) to equation (33), we
assumed a bound on the average weight enumerator Am of the form
logAm(w) ≤ B(w; dv , d
c) + o(1). (34)
For compactness in notation, we frequently write B(w), where the dependence on the degree pair
(dv , d
c) is understood implicitly. In the following paragraph, we specify a set of conditions on this
bounding function B, and we then show that under these conditions, there exists a finite degree dc
such that the compound construction achieves specified rate-distortion point. In Appendix F, we
then prove that the weight enumerator of standard regular LDPC codes satisfies the assumptions
required by our analysis.
Assumptions on weight enumerator bound We require that our bound B on the weight
enumerator satisfy the following conditions:
A1: the function B is symmetric around 1
, meaning that B(w) = B(1− w) for all w ∈ [0, 1].
A2: the function B is twice differentiable on (0, 1) with B′(1
) = 0 and B′′(1
) < 0.
A3: the function B achieves its unique optimum at w = 1
, where B(1
) = RH .
A4: there exists some ǫ1 > 0 such that B(w) < 0 for all w ∈ (0, ǫ1), meaning that the ensemble
has linear minimum distance.
In order to establish our claim, it suffices to show that for all (R,D) such that R > 1− h (D),
there exists a finite choice of dc such that
w∈[0,1]
+ F (δ∗(w; dc),D)
︸ ︷︷ ︸
 ≤ R− [1− h (D)] : = ∆ (35)
K(w; dc)
Restricting to even dc ensures that the function F is symmetric about w =
; combined with as-
sumption A2, this ensures that K is symmetric around 1
, so that we may restrict the maximization
to [0, 1
] without loss of generality. Our proof consists of the following steps:
(a) We first prove that there exists an ǫ1 > 0, independent of the choice of dc, such that
K(w; dc) ≤ ∆ for all w ∈ [0, ǫ1].
(b) We then prove that there exists ǫ2 > 0, again independent of the choice of dc, such that
K(w; dc) ≤ ∆ for all w ∈ [
− ǫ2,
(c) Finally, we specify a sufficiently large but finite degree d∗c that ensures the conditionK(w; d
c) ≤ ∆
for all w ∈ [ǫ1, ǫ2].
5.5.1 Step A
By assumption A4 (linear minimum distance), there exists some ǫ1 > 0 such that B(w) ≤ 0 for all
w ∈ [0, ǫ1]. Since F (δ
∗(w; dc);D) ≤ 0 for all w, we have K(w; dc) ≤ 0 < ∆ in this region. Note
that ǫ1 is independent of dc, since it specified entirely by the properties of the bottom code.
5.5.2 Step B
For this step of the proof, we require the following lemma on the properties of the function F :
Lemma 6. For all choices of even degrees dc ≥ 4, the function G(w; dc) = F (δ
∗(w; dc),D) is
differentiable in a neighborhood of w = 1
, with
; dc) = − [1− h (D)] , G
; dc) = 0, and G
; dc) = 0. (36)
See Appendix E for a proof of this claim. Next observe that we have the uniform bound
G(w; dc) ≤ G(w; 4) for all dc ≥ 4 and w ∈ [0,
]. This follows from the fact that F (u;D) is
decreasing in u, and that δ∗(w; 4) ≤ δ∗(w; dc) for all dc ≥ 4 and w ∈ [0,
]. Since B is independent
of dc, this implies that K(w; dc) ≤ K(w; 4) for all w ∈ [0,
]. Hence it suffices to set dc = 4, and
show that K(w; 4) ≤ ∆ for all w ∈ [1
− ǫ2,
]. Using Lemma 6, Assumption A2 concerning the
derivatives of B, and Assumption A4 (that B(1
) = RH), we have
; 4) = R− [1− h (D)] = ∆,
; 4) =
R B′(1
; 4) = 0, and
K ′′(
; 4) =
R B′′(1
+G′′(
; 4) =
R B′′(1
By the continuity of K ′′, the second derivative remains negative in a region around 1
, say for all
w ∈ [1
− ǫ2,
] for some ǫ2 > 0. Then, for all w ∈ [
− ǫ2,
], we have for some w̃ ∈ [w, 1
] the second
order expansion
K(w; 4) = K(
; 4) +K ′(
; 4)(w −
K(w̃; 4)
K(w̃; 4)
Thus, we have established that there exists an ǫ2 > 0, independent of the choice of dc, such that
for all even dc ≥ 4, we have
K(w; dc) ≤ K(w, 4) ≤ ∆ for all w ∈ [
− ǫ2,
]. (37)
5.5.3 Step C
Finally, we need to show that K(w; dc) ≤ ∆ for all w ∈ [ǫ1, ǫ2]. From assumption A3 and the
continuity of B, there exists some ρ(ǫ2) > 0 such that
B(w) ≤ RH [1− ρ(ǫ2)] for all w ≤
− ǫ2. (38)
From Lemma 6, limu→ 1
F (u;D) = F (1
;D) = − [1− h (D)]. Moreover, as dc → +∞, we have
δ∗(ǫ1; dc) →
. Therefore, for any ǫ3 > 0, there exists a finite degree d
c such that
F (δ∗(ǫ1; d
c);D) ≤ − [1− h (D)] + ǫ3.
Since F is non-increasing in w, we have F (δ∗(w; d∗c);D) ≤ − [1− h (D)] + ǫ3 for all w ∈ [ǫ1, ǫ2].
Putting together this bound with the earlier bound (38) yields that for all w ∈ [ǫ1, ǫ2]:
K(w; dc) = R
+ F (δ∗(w; d∗c),D)
≤ R [1− ρ(ǫ2)]− [1− h (D)] + ǫ3
= {R− [1− h (D)]}+ (ǫ3 − Rρ(ǫ2))
= ∆ + (ǫ3 − Rρ(ǫ2))
Since we are free to choose ǫ3 > 0, we may set ǫ3 =
Rρ(ǫ2)
to yield the claim.
6 Proof of channel coding optimality
In this section, we turn to the proof of the previously stated Theorem 2, concerning the channel
coding optimality of the compound construction.
If the codeword x ∈ {0, 1}n is transmitted, then the receiver observes V = x ⊕ W , where W
is a Ber(p) random vector. Our goal is to bound the probability that maximum likelihood (ML)
decoding fails where the probability is taken over the randomness in both the channel noise and
the code construction. To simplify the analysis, we focus on the following sub-optimal (non-ML)
decoding procedure. Let ǫn be any non-negative sequence such that ǫn/n → 0 but ǫ
n/n → +∞—say
for instance, ǫn = n
Definition 2 (Decoding Rule:). With the threshold d(n) := pn+ ǫn, decode to codeword xi ⇐⇒
‖xi ⊕ V ‖1 ≤ d(n), and no other codeword is within d(n) of V .
The extra term ǫn in the threshold d(n) is chosen for theoretical convenience. Using the following
two lemmas, we establish that this procedure has arbitrarily small probability of error, whence ML
decoding (which is at least as good) also has arbitrarily small error probability.
Lemma 7. Using the suboptimal procedure specified in the definition (2), the probability of decoding
error vanishes asymptotically provided that
RG B(w)−D (p||δ
∗(w; dc) ∗ p) < 0 for all w ∈ (0,
], (39)
where B is any function bounding the average weight enumerator as in equation (34).
Proof. Let N = 2nR = 2mRH denote the total number of codewords in the joint LDGM/LDPC
code. Due to the linearity of the code construction and symmetry of the decoding procedure, we
may assume without loss of generality that the all zeros codeword 0n was transmitted (i.e., x = 0n).
In this case, the channel output is simply V = W and so our decoding procedure will fail if and
only if one the following two conditions holds:
(i) either ‖W‖1 > d(n), or
(ii) there exists a sequence of information bits y ∈ {0, 1}m satisfying the parity check equation
Hy = 0 such that the codeword Gy satisfies ‖Gy ⊕W‖1 ≤ d(n).
Consequently, using the union bound, we can upper bound the probability of error as follows:
perr ≤ P[‖W‖1 > d(n)] +
‖Gyi ⊕W‖1 ≤ d(n)
. (40)
Since E[‖W‖1] = pn, we may apply Hoeffdings’s inequality [13] to conclude that
P[‖W‖1 > d(n)] ≤ 2 exp
→ 0 (41)
by our choice of ǫn. Now focusing on the second term, let us rewrite it as a sum over the possible
Hamming weights ℓ = 1, 2, . . . ,m of information sequences (i.e., ‖y‖1 = ℓ) as follows:
‖Gyi ⊕W‖1 ≤ d(n)
‖Gy ⊕W‖1 ≥ d(n)
∣∣ ‖y‖1 = ℓ
where we have used the fact that the (average) number of information sequences with fractional
weight ℓ/m is given by the LDPC weight enumerator Am(
). Analyzing the probability terms
in this sum, we note Lemma 9 (see Appendix A) guarantees that Gy has i.i.d. Ber(δ∗( ℓ
; dc))
elements, where δ∗( · ; dc) was defined in equation (29). Consequently, the vector Gy⊕W has i.i.d.
Ber(δ( ℓ
) ∗ p) elements. Applying Sanov’s theorem [11] for the special case of binomial variables
yields that for any information bit sequence y with ℓ ones, we have
‖Gy ⊕W‖1 ≥ d(n)
∣∣ ‖y‖1 = ℓ
≤ f(n)2−nD(p||δ(
)∗p), (42)
for some polynomial term f(n). We can then upper bound the second term in the error bound (40)
‖Gyi ⊕W‖1 ≤ d(n)
≤ f(m) exp
1≤ℓ≤m
) + o(m)− nD
p||δ(
) ∗ p
where we have used equation (42), as well as the assumed upper bound (34) on Am in terms of B.
Simplifying further, we take logarithms and rescale by m to assess the exponential rate of decay,
thereby obtaining
‖Gyi ⊕W‖1 ≤ d(n)
≤ max
1≤ℓ≤m
p||δ(
) ∗ p
+ o(1)
≤ max
w∈[0,1]
B(w)−
D (p||δ(w) ∗ p)
+ o(1),
and establishing the claim.
Lemma 8. For any p ∈ (0, 1) and total rate R : = RGRH < 1 − h (p), there exist finite choices
of the degree triplet (dc, dv , d
c) such that (39) is satisfied.
Proof. For notational convenience, we define
L(w) := RGB(w)−D (p||δ
∗(w; dc) ∗ p) . (43)
First of all, it is known [17] that a regular LDPC code with rate RH =
< 1 and dv ≥ 3 has linear
minimum distance. More specifically, there exists a threshold ν∗ = ν∗(dv, dc) such that B(w) ≤ 0
for all w ∈ [0, ν∗]. Hence, since B(w)−D (p||δ∗(w; dc) ∗ p) ≥ 0 for all w ∈ (0, 1), for w ∈ (0, ν
∗], we
have L(w) < 0.
Turning now to the interval [ν∗, 1
], consider the function
L̃(w) := Rh (w)−D (p||δ∗(w; dc) ∗ p) . (44)
Since B(w) ≤ RHh (w), we have L(w) ≤ L̃(w), so that it suffices to upper bound L̃. Observe that
) = R − (1 − h (p)) < 0 by assumption. Therefore, it suffices to show that, by appropriate
choice of dc, we can ensure that L̃(w) ≤ L̃(
). Noting that L̃ is infinitely differentiable, calculating
derivatives yields L̃′(1
) = 0 and L̃′′(1
) < 0. (See Appendix G for details of these derivative
calculations.) Hence, by second order Taylor series expansion around w = 1
, we obtain
L̃(w) = L̃(
L̃′′(w̄)(w −
where w̄ ∈ [w, 1
]. By continuity of L̃′′, we have L̃′′(w) < 0 for all w in some neighborhood of 1
so that the Taylor series expansion implies that L̃(w) ≤ L̃(1
) for all w in some neighborhood, say
(µ, 1
It remains to bound L̃ on the interval [ν∗, µ]. On this interval, we have L̃(w) ≤ Rh (µ) −
D (p||δ∗(ν∗; dc) ∗ p). By examining equation (29) from Lemma 9, we see that by choosing dc
sufficiently large, we can make δ∗(ν∗; dc) arbitrarily close to
, and hence D (p||δ∗(ν∗; dc) ∗ p)
arbitrarily close to 1 − h (p). More precisely, let us choose dc large enough to guarantee that
D (p||δ∗(ν∗; dc) ∗ p) < (1 − ǫ) (1 − h (p)), where ǫ =
R (1−h(µ))
1−h(p) . With this choice, we have, for all
w ∈ [ν∗, µ], the sequence of inequalities
L̃(w) ≤ Rh (µ)−D (p||δ∗(ν∗; dc) ∗ p)
< Rh (µ)−
(1− h (p))−R(1− h (µ))
= R− (1− h (p)) < 0,
which completes the proof.
7 Discussion
In this paper, we established that it is possible to achieve both the rate-distortion bound for
symmetric Bernoulli sources and the channel capacity for the binary symmetric channel using
codes with bounded graphical complexity. More specifically, we have established that there exist
low-density generator matrix (LDGM) codes and low-density parity check (LDPC) codes with
finite degrees that, when suitably compounded to form a new code, are optimal for both source
and channel coding. To the best of our knowledge, this is the first demonstration of classes of codes
with bounded graphical complexity that are optimal as source and channel codes simultaneously.
We also demonstrated that this compound construction has a naturally nested structure that can
be exploited to achieve the Wyner-Ziv bound [45] for lossy compression of binary data with side
information, as well as the Gelfand-Pinsker bound [19] for channel coding with side information.
Since the analysis of this paper assumed optimal decoding and encoding, the natural next step
is the development and analysis of computationally efficient algorithms for encoding and decoding.
Encouragingly, the bounded graphical complexity of our proposed codes ensures that they will, with
high probability, have high girth and good expansion, thus rendering them well-suited to message-
passing and other efficient decoding procedures. For pure channel coding, previous work [16, 36, 41]
has analyzed the performance of belief propagation when applied to various types of compound
codes, similar to those analyzed in this paper. On the other hand, for pure lossy source coding, our
own past work [44] provides empirical demonstration of the feasibility of modified message-passing
schemes for decoding of standard LDGM codes. It remains to extend both these techniques and their
analysis to more general joint source/channel coding problems, and the compound constructions
analyzed in this paper.
Acknowledgements
The work of MJW was supported by National Science Foundation grant CAREER-CCF-0545862,
a grant from Microsoft Corporation, and an Alfred P. Sloan Foundation Fellowship.
A Basic property of LDGM codes
For a given weight w ∈ (0, 1), suppose that we enforce that the information sequence y ∈ {0, 1}m
has exactly ⌈wm⌉ ones. Conditioned on this event, we can then consider the set of all codewords
X(w) ∈ {0, 1}n, where we randomize over low-density generator matrices G chosen as in step (a)
above. Note for any fixed code, X(w) is simply some codeword, but becomes a random variable
when we imagine choosing the generator matrix G randomly. The following lemma characterizes
this distribution as a function of the weight w and the LDGM top degree dc:
Lemma 9. Given a binary vector y ∈ {0, 1}m with a fraction w of ones, the distribution of
the random LDGM codeword X(w) induced by y is i.i.d. Bernoulli with parameter δ∗(w; dc) =
1− (1− 2w)dc
Proof. Given a fixed sequence y ∈ {0, 1}m with a fraction w ones, the random codeword bit Xi(w)
at bit i is formed by connecting dc edges to the set of information bits.
3 Each edge acts as an i.i.d.
Bernoulli variable with parameter w, so that we can write
Xi(w) = V1 ⊕ V2 ⊕ . . .⊕ Vdc , (45)
where each Vk ∼ Ber(w) is independent and identically distributed. A straightforward calculation
using z-transforms (see [17]) or Fourier transforms over GF (2) yields that Xi(w) is Bernoulli with
parameter δ∗(w; dc) as defined.
B Bounds on binomial coefficients
The following bounds on binomial coefficients are standard (see Chap. 12, [11]):
log(n+ 1)
. (46)
Here, for α ∈ (0, 1), the quantity h(α) := −α logα − (1 − α) log(1 − α) is the binomial entropy
function.
3In principle, our procedure allows two different edges to choose the same information bit, but the probability of
such double-edges is asymptotically negligible.
C Proof of Lemma 4
First, by the definition of Tn(D), we have
E[T 2n(D)] = E
Zi(D)Zj(D)
= E[Tn] +
j 6=i
P[Zi(D) = 1, Zi(D) = 1].
To simplify the second term on the RHS, we first note that for any i.i.d Bernoulli(1
) sequence
S ∈ {0, 1}n and any codeword Xj , the binary sequence S′ : = S ⊕ Xj is also i.i.d. Bernoulli(1
Consequently, for each pair i 6= j, we have
Zi(D) = 1, Zj(D) = 1
‖Xi ⊕ S‖1 ≤ Dn, ‖X
j ⊕ S‖1 ≤ Dn
‖Xi ⊕ S′‖1 ≤ Dn, ‖X
j ⊕ S′‖1 ≤ Dn
‖Xi ⊕Xj ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
Note that for each j 6= i, the vector Xi ⊕Xj is a non-zero codeword. For each fixed i, summing
over j 6= i can be recast as summing over all non-zero codewords, so that
i 6=j
Zi(D) = 1, Zj(D) = 1
j 6=i
‖Xi ⊕Xj ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
k 6=0
‖Xk ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
= 2nR
k 6=0
‖Xk ⊕ S‖1 ≤ Dn, ‖S‖1 ≤ Dn
= 2nRP
Z0(D) = 1
k 6=0
Zk(D) = 1 | Z0(D) = 1
= E[Tn]
k 6=0
Zk(D) = 1 | Z0(D)
thus establishing the claim.
D Proof of Lemma 5
We reformulate the probability Q(w,D) as follows. Recall that Q involves conditioning the source
sequence S on the event ‖S‖1 ≤ Dn. Accordingly, we define a discrete variable T with distribution
P(T = t) =
) for t = 0, 1, . . . ,Dn,
representing the (random) number of 1s in the source sequence S. Let Ui and Vj denote Bernoulli
random variables with parameters 1 − δ∗(w; dc) and δ
∗(w; dc) respectively. With this set-up, con-
ditioned on codeword j having a fraction wn ones, the quantity Q(w,D) is equivalent to the
probability that the random variable
W : =
i=1 Uj +
j=1 Vj if T ≥ 1
j=1 Vj if T = 0
is less than Dn. To bound this probability, we use a Chernoff bound in the form
logP[W ≤ Dn] ≤ inf
logMW (λ)− λD
. (48)
We begin by computing the moment generating function MW . Taking conditional expectations and
using independence, we have
MW (λ) =
P[T = t] [MU (λ)]
[MV (λ)]
Here the cumulant generating functions have the form
logMU(λ) = log
(1− δ)eλ + δ
, and (49a)
logMV (λ) = log
(1− δ) + δeλ
, (49b)
where we have used (and will continue to use) δ as a shorthand for δ∗(w; dc).
Of interest to us is the exponential behavior of this expression in n. Using the standard entropy
approximations to the binomial coefficient (see Appendix B), we can bound MW (λ) as
− h (D) +
logMU (λ) +
logMV (λ)
︸ ︷︷ ︸
, (50)
where f(n) denotes a generic polynomial factor. Further analyzing this sum, we have
g(t) ≤
0≤t≤Dn
log g(t) +
log f(n)
log(nD)
= max
0≤t≤Dn
− h (D) +
logMU (λ) +
logMV (λ)
+ o(1)
≤ max
u∈[0,D]
{h (u)− h (D) + u logMU (λ) + (1− u) logMV (λ)} + o(1).
Combining this upper bound on 1
logMW (λ) with the Chernoff bound (48) yields that
logP[W ≤ Dn] ≤ inf
u∈[0,D]
G(u, λ; δ) + o(1) (51)
where the function G takes the form
G(u, λ; δ) := h (u)− h (D) + u logMU (λ) + (1− u) logMV (λ)− λD. (52)
Finally, we establish that the solution (u∗, λ∗) to the min-max saddle point problem (51) is
unique, and specified by u∗ = D and λ∗ as in Lemma 5. First of all, observe that for any δ ∈ (0, 1),
the function G is continuous, strictly concave in u and strictly convex in λ. (The strict concavity
follows since h (u) is strictly concave with the remaining terms linear; the strict convexity follows
since cumulant generating functions are strictly convex.) Therefore, for any fixed λ < 0, the
maximum over u ∈ [0,D] is always achieved. On the other hand, for any D > 0, u ∈ [0,D] and
δ ∈ (0, 1), we have G(u;λ; t) → +∞ as λ → −∞, so that the infimum is either achieved at some
λ∗ < 0, or at λ∗ = 0. We show below that it is always achieved at an interior point λ∗ < 0. Thus
far, using standard saddle point theory [21], we have established the existence and uniqueness of
the saddle point solution (u∗, λ∗).
To verify the fixed point conditions, we compute partial derivatives in order to find the optimum.
First, considering u, we compute
(u, λ; δ) = log
+ logMU (λ)− logMV (λ)
= log
+ log
(1− δ)eλ + δ
− log
(1− δ) + δeλ
Solving the equation ∂G
(u, λ; δ) = 0 yields
exp(λ)
1 + exp(λ)
1 + exp(λ)
(1−D) ≥ 0. (53)
Since D ≤ 1
, a bit of algebra shows that u′ ≥ D for all choices of λ. Since the maximization is
constrained to [0,D], the optimum is always attained at u∗ = D.
Turning now to the minimization over λ, we compute the partial derivative to find
(u, λ; δ) = u
(1− δ) exp(λ)
(1− δ) exp(λ) + δ
+ (1− u)
δ exp(λ)
(1− δ) + δ exp(λ)
Setting this partial derivative to zero yields a quadratic equation in exp(λ) with coefficients
a = δ (1− δ) (1 −D) (54a)
b = u(1− δ)2 + (1− u)δ2 −D
δ2 + (1− δ)2
. (54b)
c = −Dδ(1− δ). (54c)
The unique positive root ρ∗ of this quadratic equation is given by
ρ∗(δ,D, u) :=
b2 − 4ac
. (55)
It remains to show that ρ∗ ≤ 1, so that λ∗ : = log ρ∗ < 0. A bit of algebra (using the fact a ≥ 0)
shows that ρ∗ < 1 if and only if a+ b+ c > 0. We then note that at the optimal u∗ = D, we have
b = (1− 2D)δ2, whence
a+ b+ c = δ (1− δ) (1 −D) + (1− 2D)δ2 −Dδ(1 − δ)
= (1− 2D) δ > 0
since D < 1
and δ > 0. Hence, the optimal solution is λ∗ : = log ρ∗ < 0, as specified in the lemma
statement.
E Proof of Lemma 6
A straightforward calculation yields that
) = F (δ∗(
; dc);D) = F (
;D) = − (1− h (D))
as claimed. Turning next to the derivatives, we note that by inspection, the solution λ∗(t) defined in
Lemma 5 is twice continuously differentiable as a function of t. Consequently, the function F (t,D)
is twice continuously differentiable in t. Moreover, the function δ∗(w; dc) is twice continuously
differentiable in w. Overall, we conclude that G(w) = F (δ∗(w; dc);D) is twice continuously differ-
entiable in w, and that we can obtain derivatives via chain rule. Computing the first derivative,
we have
) = δ′(
)F ′(δ∗(
; dc);D) = 0
since δ′(w) = −dc (1− 2w)
dc−1, which reduces to zero at w = 1
. Turning to the second derivative,
we have
) = δ′′(
)F ′(δ∗(
; dc);D) +
F ′′(δ∗(
; dc);D) = δ
)F ′(δ∗(
; dc);D).
We again compute δ′′(w) = 2dc (dc − 1)(1 − 2w)
dc−2, which again reduces to zero at w = 1
since
dc ≥ 4 by assumption.
F Regular LDPC codes are sufficient
Consider a regular (dv, d
c) code from the standard Gallager LDPC ensemble. In order to complete
the proof of Theorem 1, we need to show for suitable choices of degree (dv , d
c), the average weight
enumerator of these codes can be suitably bounded, as in equation (34), by a function B that
satisfies the conditions specified in Section 5.5.
It can be shown [17, 22] that for even degrees d′c, the average weight enumerator of the regular
Gallager ensemble, for any block length m, satisfies the bound
logAm(w) = B(w; dv , d
c) + o(1).
The function B in this relation is defined for w ∈ [0, 1
B(w; dv , d
c) := (1− dv)h (w)− (1−RH) + dv inf
(1 + eλ)d
c + (1− eλ)d
, (56)
and by B(w) = B(w − 1
) for w ∈ [1
, 1]. Given that the minimization problem (56) is strictly
convex, a straightforward calculation of the derivative shows the optimum is achieved at λ∗, where
λ∗ ≤ 0 is the unique solution of the equation
(1 + eλ)d
c−1 − (1− eλ)d
(1 + eλ)d
c + (1− eλ)d
= w. (57)
Some numerical computation for RH = 0.5 and different choices (dv, d
c) yields the curves shown
in Fig. 9.
We now show that for suitable choices of degree (dv , d
c), the function B defined in equation (56)
satisfies the four assumptions specified in Section 5.5. First, for even degrees d′c, the function B
0 0.1 0.2 0.3 0.4 0.5
Weight
LDPC weight enumerators
 = 10
Figure 9. Plots of LDPC weight enumerators for codes of rate RH = 0.5, and check degrees
∈ {6, 8, 10}.
is symmetric about w = 1
, so that assumption (A1) holds. Secondly, we have B(w) ≤ RH , and
moreover, for w = 1
, the optimal λ∗(1
) = 0, so that B(1
) = RH , and assumption (A3) is satisfied.
Next, it is known from the work of Gallager [17], and moreover is clear from the plots in Fig. 9,
that LDPC codes with dv > 2 have linear minimum distance, so that assumption (A4) holds.
The final condition to verify is assumption (A2), concerning the differentiability of B. We
summarize this claim in the following:
Lemma 10. The function B is twice continuously differentiable on (0, 1), and in particular we
) = 0, and B′′(
) < 0. (58)
Proof. Note that for each fixed w ∈ (0, 1), the function
f(λ) =
(1 + eλ)d
c + (1− eλ)d
(e−λ + 1)d
c + (e−λ − 1)d
is strictly convex and twice continuously differentiable as a function of λ. Moreover, the function
f∗(w) := infλ≤0 {f(λ)− λw} corresponds to the conjugate dual [21] of f(λ) + I≤0(λ). Since the
optimum is uniquely attained for each w ∈ (0, 1), an application of Danskin’s theorem [4] yields
that f∗ is differentiable with d
f∗(w) = −λ∗(w), where λ∗ is defined by equation (57). Putting
together the pieces, we have B′(w) = (1 − dv)h
′(w) − dvλ
∗(w). Evaluating at w = 1
yields
) = 0− dvλ
∗(0) = 0 as claimed.
We now claim that λ∗(w) is differentiable. Indeed, let us write the defining relation (57) for
λ∗(w) as F (λ,w) = 0 where F (λ,w) := f ′(λ)−w. Note that F is twice continuously differentiable
in both λ and w; moreover, ∂F
exists for all λ ≤ 0 and w, and satisfies ∂F
(λ,w) = f ′′(λ) > 0
by the strict convexity of f . Hence, applying the implicit function theorem [4] yields that λ∗(w)
is differentiable, and moreover that dλ
(w) = 1/f ′′(λ∗(w)). Hence, combined with our earlier
calculation of B′, we conclude that B′′(w) = (1 − dv)h
′′(w) − dv
f ′′(λ(w))
. Our final step is to
compute the second derivative f ′′. In order to do so, it is convenient to define g = log f ′, and
exploit the relation g′f ′ = f ′′. By definition, we have
g(λ) = λ+ log
(1 + eλ)d
c−1 − (1− eλ)d
− log
(1 + eλ)d
c + (1− eλ)d
whence
g′(λ) = 1 + eλ(d′c − 1)
(1 + eλ)d
c−2 + (1− eλ)d
(1 + eλ)d
c−1 − (1− eλ)d
− eλd′c
(1 + eλ)d
c−1 − (1− eλ)d
(1 + eλ)d
c + (1− eλ)d
Evaluating at w = 1
corresponds to λ(0) = 0, so that
f ′′(λ(
)) = f ′(0) g′(0) =
1 + (d′c − 1)
− d′c
Consequently, combining all of the pieces, we have
B′′(w) = (1− dv)h
)− dv
f ′′(λ(1
dv − 1
− 4dv < 0
as claimed.
G Derivatives of L̃
Here we calculate the first and second derivatives of the function L̃ defined in equation (44). The
first derivative takes the form
L̃′(v) = R log
δ′(v; dc)
δ(v; dc)
− (1− p)
δ′(v; dc)
1− δ(v; dc)
where δ′(v; dc) = dc(1 − 2v)
dc−1. Since δ′(1
; dc) = 0, we have L̃
) = 0 as claimed. Second, using
chain rule, we calculate
L̃′′(v) = −R
δ′′(v; dc)δ(v; dc)− [δ
′(v; dc)]
[δ(v; dc)]
− (1− p)
δ′′(v; dc)
1− δ(v; dc)
+ [δ′(v; dc)]
[1− δ(v; dc)]2
and δ′′(v; dc) = −dc (dc − 1) (1 − 2v)
dc−2. Now for dc > 2, we have δ
) = 0, so that L̃′′(1
−4R < 0 as claimed.
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	Introduction
	Previous and ongoing work
	Our contributions
	Background
	Source and channel coding
	Factor graphs and graphical codes
	Weight enumerating functions
	Optimality of bounded degree compound constructions
	Compound construction
	Main results
	Consequences for coding with side information
	Nested code structure
	Source coding with side information
	Problem formulation
	Coding procedure for SCSI
	Channel coding with side information
	Problem formulation
	Coding procedure for CCSI
	Proof of source coding optimality
	Set-up
	Moment analysis
	Second moment analysis
	Bounding the overlap probability
	Finite degrees are sufficient
	Step A
	Step B
	Step C
	Proof of channel coding optimality
	Discussion
	Basic property of LDGM codes
	Bounds on binomial coefficients
	Proof of Lemma ??
	Proof of Lemma ??
	Proof of Lemma ??
	Regular LDPC codes are sufficient
	Derivatives of L"0365L
