425 research outputs found
Capacity and Modulations with Peak Power Constraint
A practical communication channel often suffers from constraints on input
other than the average power, such as the peak power constraint. In order to
compare achievable rates with different constellations as well as the channel
capacity under such constraints, it is crucial to take these constraints into
consideration properly. In this paper, we propose a direct approach to compare
the achievable rates of practical input constellations and the capacity under
such constraints. As an example, we study the discrete-time complex-valued
additive white Gaussian noise (AWGN) channel and compare the capacity under the
peak power constraint with the achievable rates of phase shift keying (PSK) and
quadrature amplitude modulation (QAM) input constellations.Comment: 9 pages with 12 figures. Preparing for submissio
Neural style transfer of weak lensing mass maps
We propose a new generative model of projected cosmic mass density maps
inferred from weak gravitational lensing observations of distant galaxies (weak
lensing mass maps). We construct the model based on a neural style transfer so
that it can transform Gaussian weak lensing mass maps into deeply non-Gaussian
counterparts as predicted in ray-tracing lensing simulations. We develop an
unpaired image-to-image translation method with Cycle-Consistent Generative
Adversarial Networks (Cycle GAN), which learn efficient mapping from an input
domain to a target domain. Our model is designed to enjoy important advantages;
it is trainable with no need for paired simulation data, flexible to make the
input domain visually meaningful, and expandable to rapidly-produce a map with
a larger sky coverage than training data without additional learning. Using
10,000 lensing simulations, we find that appropriate labeling of training data
based on field variance requires the model to exhibit a desired diversity of
various summary statistics for weak lensing mass maps. Compared with a popular
log-normal model, our model improves in predicting the statistical natures of
three-point correlations and local properties of rare high-density regions. We
also demonstrate that our model enables us to produce a continuous map with a
sky coverage of but similar non-Gaussian features to
training data covering in a GPU minute. Hence, our
model can be beneficial to massive productions of synthetic weak lensing mass
maps, which is of great importance in future precise real-world analyses.Comment: 20 pages, 11 figures, 3 tables. A trial dataset of fake weak lensing
mass maps generated by our GANs is available at
https://www.dropbox.com/scl/fo/hq1o41e8jwsfm4gqtkmnu/h?rlkey=0ymsucz2nzoju3gew8tsyz7qw&dl=
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