5,438 research outputs found
Capacity-Achieving Codes with Bounded Graphical Complexity on Noisy Channels
We introduce a new family of concatenated codes with an outer low-density
parity-check (LDPC) code and an inner low-density generator matrix (LDGM) code,
and prove that these codes can achieve capacity under any memoryless
binary-input output-symmetric (MBIOS) channel using maximum-likelihood (ML)
decoding with bounded graphical complexity, i.e., the number of edges per
information bit in their graphical representation is bounded. In particular, we
also show that these codes can achieve capacity on the binary erasure channel
(BEC) under belief propagation (BP) decoding with bounded decoding complexity
per information bit per iteration for all erasure probabilities in (0, 1). By
deriving and analyzing the average weight distribution (AWD) and the
corresponding asymptotic growth rate of these codes with a rate-1 inner LDGM
code, we also show that these codes achieve the Gilbert-Varshamov bound with
asymptotically high probability. This result can be attributed to the presence
of the inner rate-1 LDGM code, which is demonstrated to help eliminate high
weight codewords in the LDPC code while maintaining a vanishingly small amount
of low weight codewords.Comment: 17 pages, 2 figures. This paper is to be presented in the 43rd Annual
Allerton Conference on Communication, Control and Computing, Monticello, IL,
USA, Sept. 28-30, 200
On Universal Properties of Capacity-Approaching LDPC Ensembles
This paper is focused on the derivation of some universal properties of
capacity-approaching low-density parity-check (LDPC) code ensembles whose
transmission takes place over memoryless binary-input output-symmetric (MBIOS)
channels. Properties of the degree distributions, graphical complexity and the
number of fundamental cycles in the bipartite graphs are considered via the
derivation of information-theoretic bounds. These bounds are expressed in terms
of the target block/ bit error probability and the gap (in rate) to capacity.
Most of the bounds are general for any decoding algorithm, and some others are
proved under belief propagation (BP) decoding. Proving these bounds under a
certain decoding algorithm, validates them automatically also under any
sub-optimal decoding algorithm. A proper modification of these bounds makes
them universal for the set of all MBIOS channels which exhibit a given
capacity. Bounds on the degree distributions and graphical complexity apply to
finite-length LDPC codes and to the asymptotic case of an infinite block
length. The bounds are compared with capacity-approaching LDPC code ensembles
under BP decoding, and they are shown to be informative and are easy to
calculate. Finally, some interesting open problems are considered.Comment: Published in the IEEE Trans. on Information Theory, vol. 55, no. 7,
pp. 2956 - 2990, July 200
Low-Density Parity-Check Codes for Nonergodic Block-Fading Channels
We solve the problem of designing powerful low-density parity-check (LDPC)
codes with iterative decoding for the block-fading channel. We first study the
case of maximum-likelihood decoding, and show that the design criterion is
rather straightforward. Unfortunately, optimal constructions for
maximum-likelihood decoding do not perform well under iterative decoding. To
overcome this limitation, we then introduce a new family of full-diversity LDPC
codes that exhibit near-outage-limit performance under iterative decoding for
all block-lengths. This family competes with multiplexed parallel turbo codes
suitable for nonergodic channels and recently reported in the literature.Comment: Submitted to the IEEE Transactions on Information Theor
Superadditivity of Quantum Channel Coding Rate with Finite Blocklength Joint Measurements
The maximum rate at which classical information can be reliably transmitted
per use of a quantum channel strictly increases in general with , the number
of channel outputs that are detected jointly by the quantum joint-detection
receiver (JDR). This phenomenon is known as superadditivity of the maximum
achievable information rate over a quantum channel. We study this phenomenon
for a pure-state classical-quantum (cq) channel and provide a lower bound on
, the maximum information rate when the JDR is restricted to making
joint measurements over no more than quantum channel outputs, while
allowing arbitrary classical error correction. We also show the appearance of a
superadditivity phenomenon---of mathematical resemblance to the aforesaid
problem---in the channel capacity of a classical discrete memoryless channel
(DMC) when a concatenated coding scheme is employed, and the inner decoder is
forced to make hard decisions on -length inner codewords. Using this
correspondence, we develop a unifying framework for the above two notions of
superadditivity, and show that for our lower bound to to be equal to a
given fraction of the asymptotic capacity of the respective channel,
must be proportional to , where is the respective channel dispersion
quantity.Comment: To appear in IEEE Transactions on Information Theor
- …