494 research outputs found
A Simple Proof of Maxwell Saturation for Coupled Scalar Recursions
Low-density parity-check (LDPC) convolutional codes (or spatially-coupled
codes) were recently shown to approach capacity on the binary erasure channel
(BEC) and binary-input memoryless symmetric channels. The mechanism behind this
spectacular performance is now called threshold saturation via spatial
coupling. This new phenomenon is characterized by the belief-propagation
threshold of the spatially-coupled ensemble increasing to an intrinsic noise
threshold defined by the uncoupled system. In this paper, we present a simple
proof of threshold saturation that applies to a wide class of coupled scalar
recursions. Our approach is based on constructing potential functions for both
the coupled and uncoupled recursions. Our results actually show that the fixed
point of the coupled recursion is essentially determined by the minimum of the
uncoupled potential function and we refer to this phenomenon as Maxwell
saturation. A variety of examples are considered including the
density-evolution equations for: irregular LDPC codes on the BEC, irregular
low-density generator matrix codes on the BEC, a class of generalized LDPC
codes with BCH component codes, the joint iterative decoding of LDPC codes on
intersymbol-interference channels with erasure noise, and the compressed
sensing of random vectors with i.i.d. components.Comment: This article is an extended journal version of arXiv:1204.5703 and
has now been accepted to the IEEE Transactions on Information Theory. This
version adds additional explanation for some details and also corrects a
number of small typo
Density Evolution for Asymmetric Memoryless Channels
Density evolution is one of the most powerful analytical tools for
low-density parity-check (LDPC) codes and graph codes with message passing
decoding algorithms. With channel symmetry as one of its fundamental
assumptions, density evolution (DE) has been widely and successfully applied to
different channels, including binary erasure channels, binary symmetric
channels, binary additive white Gaussian noise channels, etc. This paper
generalizes density evolution for non-symmetric memoryless channels, which in
turn broadens the applications to general memoryless channels, e.g. z-channels,
composite white Gaussian noise channels, etc. The central theorem underpinning
this generalization is the convergence to perfect projection for any fixed size
supporting tree. A new iterative formula of the same complexity is then
presented and the necessary theorems for the performance concentration theorems
are developed. Several properties of the new density evolution method are
explored, including stability results for general asymmetric memoryless
channels. Simulations, code optimizations, and possible new applications
suggested by this new density evolution method are also provided. This result
is also used to prove the typicality of linear LDPC codes among the coset code
ensemble when the minimum check node degree is sufficiently large. It is shown
that the convergence to perfect projection is essential to the belief
propagation algorithm even when only symmetric channels are considered. Hence
the proof of the convergence to perfect projection serves also as a completion
of the theory of classical density evolution for symmetric memoryless channels.Comment: To appear in the IEEE Transactions on Information Theor
Spatially-Coupled Precoded Rateless Codes
Raptor codes are rateless codes that achieve the capacity on the binary
erasure channels. However the maximum degree of optimal output degree
distribution is unbounded. This leads to a computational complexity problem
both at encoders and decoders. Aref and Urbanke investigated the potential
advantage of universal achieving-capacity property of proposed
spatially-coupled (SC) low-density generator matrix (LDGM) codes. However the
decoding error probability of SC-LDGM codes is bounded away from 0. In this
paper, we investigate SC-LDGM codes concatenated with SC low-density
parity-check codes. The proposed codes can be regarded as SC Hsu-Anastasopoulos
rateless codes. We derive a lower bound of the asymptotic overhead from
stability analysis for successful decoding by density evolution. The numerical
calculation reveals that the lower bound is tight. We observe that with a
sufficiently large number of information bits, the asymptotic overhead and the
decoding error rate approach 0 with bounded maximum degree
Nonbinary Spatially-Coupled LDPC Codes on the Binary Erasure Channel
We analyze the asymptotic performance of nonbinary spatially-coupled
low-density parity-check (SC-LDPC) codes built on the general linear group,
when the transmission takes place over the binary erasure channel. We propose
an efficient method to derive an upper bound to the maximum a posteriori
probability (MAP) threshold for nonbinary LDPC codes, and observe that the MAP
performance of regular LDPC codes improves with the alphabet size. We then
consider nonbinary SC-LDPC codes. We show that the same threshold saturation
effect experienced by binary SC-LDPC codes occurs for the nonbinary codes,
hence we conjecture that the BP threshold for large termination length
approaches the MAP threshold of the underlying regular ensemble.Comment: Submitted to IEEE International Conference on Communications 201
Capacity-achieving ensembles for the binary erasure channel with bounded complexity
We present two sequences of ensembles of non-systematic irregular
repeat-accumulate codes which asymptotically (as their block length tends to
infinity) achieve capacity on the binary erasure channel (BEC) with bounded
complexity per information bit. This is in contrast to all previous
constructions of capacity-achieving sequences of ensembles whose complexity
grows at least like the log of the inverse of the gap (in rate) to capacity.
The new bounded complexity result is achieved by puncturing bits, and allowing
in this way a sufficient number of state nodes in the Tanner graph representing
the codes. We also derive an information-theoretic lower bound on the decoding
complexity of randomly punctured codes on graphs. The bound holds for every
memoryless binary-input output-symmetric channel and is refined for the BEC.Comment: 47 pages, 9 figures. Submitted to IEEE Transactions on Information
Theor
Generalized Stability Condition for Generalized and Doubly-Generalized LDPC Codes
In this paper, the stability condition for low-density parity-check (LDPC)
codes on the binary erasure channel (BEC) is extended to generalized LDPC
(GLDPC) codes and doublygeneralized LDPC (D-GLDPC) codes. It is proved that, in
both cases, the stability condition only involves the component codes with
minimum distance 2. The stability condition for GLDPC codes is always expressed
as an upper bound to the decoding threshold. This is not possible for D-GLDPC
codes, unless all the generalized variable nodes have minimum distance at least
3. Furthermore, a condition called derivative matching is defined in the paper.
This condition is sufficient for a GLDPC or DGLDPC code to achieve the
stability condition with equality. If this condition is satisfied, the
threshold of D-GLDPC codes (whose generalized variable nodes have all minimum
distance at least 3) and GLDPC codes can be expressed in closed form.Comment: 5 pages, 2 figures, to appear in Proc. of IEEE ISIT 200
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