4,769 research outputs found
Highly Robust Error Correction by Convex Programming
This paper discusses a stylized communications problem where one wishes to transmit a real-valued signal x ∈ ℝ^n (a block of n pieces of information) to a remote receiver. We ask whether it is possible to transmit this information reliably when a fraction of the transmitted codeword is corrupted by arbitrary gross errors, and when in addition, all the entries of the codeword are contaminated by smaller errors (e.g., quantization errors).
We show that if one encodes the information as Ax where A ∈
ℝ^(m x n) (m ≥ n) is a suitable coding matrix, there are two decoding schemes that allow the recovery of the block of n pieces of information x with nearly the same accuracy as if no gross errors occurred upon transmission (or equivalently as if one had an oracle supplying perfect information about the sites and amplitudes of the gross errors). Moreover, both decoding strategies are very concrete and only involve solving simple convex optimization programs, either a linear program or a second-order cone program. We complement our study with numerical simulations showing that the encoder/decoder pair performs remarkably well
Measurement-free topological protection using dissipative feedback
Protecting quantum information from decoherence due to environmental noise is
vital for fault-tolerant quantum computation. To this end, standard quantum
error correction employs parallel projective measurements of individual
particles, which makes the system extremely complicated. Here we propose
measurement-free topological protection in two dimension without any selective
addressing of individual particles. We make use of engineered dissipative
dynamics and feedback operations to reduce the entropy generated by decoherence
in such a way that quantum information is topologically protected. We calculate
an error threshold, below which quantum information is protected, without
assuming selective addressing, projective measurements, nor instantaneous
classical processing. All physical operations are local and translationally
invariant, and no parallel projective measurement is required, which implies
high scalability. Furthermore, since the engineered dissipative dynamics we
utilized has been well studied in quantum simulation, the proposed scheme can
be a promising route progressing from quantum simulation to fault-tolerant
quantum information processing.Comment: 17pages, 6 figure
Decomposition Methods for Large Scale LP Decoding
When binary linear error-correcting codes are used over symmetric channels, a
relaxed version of the maximum likelihood decoding problem can be stated as a
linear program (LP). This LP decoder can be used to decode error-correcting
codes at bit-error-rates comparable to state-of-the-art belief propagation (BP)
decoders, but with significantly stronger theoretical guarantees. However, LP
decoding when implemented with standard LP solvers does not easily scale to the
block lengths of modern error correcting codes. In this paper we draw on
decomposition methods from optimization theory, specifically the Alternating
Directions Method of Multipliers (ADMM), to develop efficient distributed
algorithms for LP decoding.
The key enabling technical result is a "two-slice" characterization of the
geometry of the parity polytope, which is the convex hull of all codewords of a
single parity check code. This new characterization simplifies the
representation of points in the polytope. Using this simplification, we develop
an efficient algorithm for Euclidean norm projection onto the parity polytope.
This projection is required by ADMM and allows us to use LP decoding, with all
its theoretical guarantees, to decode large-scale error correcting codes
efficiently.
We present numerical results for LDPC codes of lengths more than 1000. The
waterfall region of LP decoding is seen to initiate at a slightly higher
signal-to-noise ratio than for sum-product BP, however an error floor is not
observed for LP decoding, which is not the case for BP. Our implementation of
LP decoding using ADMM executes as fast as our baseline sum-product BP decoder,
is fully parallelizable, and can be seen to implement a type of message-passing
with a particularly simple schedule.Comment: 35 pages, 11 figures. An early version of this work appeared at the
49th Annual Allerton Conference, September 2011. This version to appear in
IEEE Transactions on Information Theor
Replacing the Soft FEC Limit Paradigm in the Design of Optical Communication Systems
The FEC limit paradigm is the prevalent practice for designing optical
communication systems to attain a certain bit-error rate (BER) without forward
error correction (FEC). This practice assumes that there is an FEC code that
will reduce the BER after decoding to the desired level. In this paper, we
challenge this practice and show that the concept of a channel-independent FEC
limit is invalid for soft-decision bit-wise decoding. It is shown that for low
code rates and high order modulation formats, the use of the soft FEC limit
paradigm can underestimate the spectral efficiencies by up to 20%. A better
predictor for the BER after decoding is the generalized mutual information,
which is shown to give consistent post-FEC BER predictions across different
channel conditions and modulation formats. Extensive optical full-field
simulations and experiments are carried out in both the linear and nonlinear
transmission regimes to confirm the theoretical analysis
Near-Optimal Noisy Group Testing via Separate Decoding of Items
The group testing problem consists of determining a small set of defective
items from a larger set of items based on a number of tests, and is relevant in
applications such as medical testing, communication protocols, pattern
matching, and more. In this paper, we revisit an efficient algorithm for noisy
group testing in which each item is decoded separately (Malyutov and Mateev,
1980), and develop novel performance guarantees via an information-theoretic
framework for general noise models. For the special cases of no noise and
symmetric noise, we find that the asymptotic number of tests required for
vanishing error probability is within a factor of the
information-theoretic optimum at low sparsity levels, and that with a small
fraction of allowed incorrectly decoded items, this guarantee extends to all
sublinear sparsity levels. In addition, we provide a converse bound showing
that if one tries to move slightly beyond our low-sparsity achievability
threshold using separate decoding of items and i.i.d. randomized testing, the
average number of items decoded incorrectly approaches that of a trivial
decoder.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
Highly robust error correction by convex programming
This paper discusses a stylized communications problem where one wishes to
transmit a real-valued signal x in R^n (a block of n pieces of information) to
a remote receiver. We ask whether it is possible to transmit this information
reliably when a fraction of the transmitted codeword is corrupted by arbitrary
gross errors, and when in addition, all the entries of the codeword are
contaminated by smaller errors (e.g. quantization errors).
We show that if one encodes the information as Ax where A is a suitable m by
n coding matrix (m >= n), there are two decoding schemes that allow the
recovery of the block of n pieces of information x with nearly the same
accuracy as if no gross errors occur upon transmission (or equivalently as if
one has an oracle supplying perfect information about the sites and amplitudes
of the gross errors). Moreover, both decoding strategies are very concrete and
only involve solving simple convex optimization programs, either a linear
program or a second-order cone program. We complement our study with numerical
simulations showing that the encoder/decoder pair performs remarkably well.Comment: 23 pages, 2 figure
Numerical and analytical bounds on threshold error rates for hypergraph-product codes
We study analytically and numerically decoding properties of finite rate
hypergraph-product quantum LDPC codes obtained from random (3,4)-regular
Gallager codes, with a simple model of independent X and Z errors. Several
non-trival lower and upper bounds for the decodable region are constructed
analytically by analyzing the properties of the homological difference, equal
minus the logarithm of the maximum-likelihood decoding probability for a given
syndrome. Numerical results include an upper bound for the decodable region
from specific heat calculations in associated Ising models, and a minimum
weight decoding threshold of approximately 7%.Comment: 14 pages, 5 figure
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