20,538 research outputs found
Improved Successive Cancellation Flip Decoding of Polar Codes Based on Error Distribution
Polar codes are a class of linear block codes that provably achieves channel
capacity, and have been selected as a coding scheme for generation
wireless communication standards. Successive-cancellation (SC) decoding of
polar codes has mediocre error-correction performance on short to moderate
codeword lengths: the SC-Flip decoding algorithm is one of the solutions that
have been proposed to overcome this issue. On the other hand, SC-Flip has a
higher implementation complexity compared to SC due to the required
log-likelihood ratio (LLR) selection and sorting process. Moreover, it requires
a high number of iterations to reach good error-correction performance. In this
work, we propose two techniques to improve the SC-Flip decoding algorithm for
low-rate codes, based on the observation of channel-induced error
distributions. The first one is a fixed index selection (FIS) scheme to avoid
the substantial implementation cost of LLR selection and sorting with no cost
on error-correction performance. The second is an enhanced index selection
(EIS) criterion to improve the error-correction performance of SC-Flip
decoding. A reduction of in the implementation cost of logic elements
is estimated with the FIS approach, while simulation results show that EIS
leads to an improvement on error-correction performance improvement up to
dB at a target FER of .Comment: This version of the manuscript corrects an error in the previous
ArXiv version, as well as the published version in IEEE Xplore under the same
title, which has the DOI:10.1109/WCNCW.2018.8368991. The corrections include
all the simulations of SC-Flip-based and SC-Oracle decoders, along with
associated comments in-tex
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural network
models are key aspects of both biological and hardware modeling of learning
processes. Here we show that a neural network model with stochastic binary
weights naturally gives prominence to exponentially rare dense regions of
solutions with a number of desirable properties such as robustness and good
generalization performance, while typical solutions are isolated and hard to
find. Binary solutions of the standard perceptron problem are obtained from a
simple gradient descent procedure on a set of real values parametrizing a
probability distribution over the binary synapses. Both analytical and
numerical results are presented. An algorithmic extension aimed at training
discrete deep neural networks is also investigated.Comment: 7 pages + 14 pages of supplementary materia
The chemistry of comets An annotated bibliography
Annotated bibliography on chemistry of comets - free radicals, photochemistry, photolysis, and spectral analysi
Finite-size scaling and deconfinement transition: the case of 4D SU(2) pure gauge theory
A recently introduced method for determining the critical indices of the
deconfinement transition in gauge theories, already tested for the case of 3D
SU(3) pure gauge theory, is applied here to 4D SU(2) pure gauge theory. The
method is inspired by universality and based on the finite size scaling
behavior of the expectation value of simple lattice operators, such as the
plaquette. We obtain an accurate determination of the critical index , in
agreement with the prediction of the Svetitsky-Yaffe conjecture.Comment: 11 pages, 3 eps figure
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