1,443 research outputs found
Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures
We consider the problem of channel estimation for millimeter wave (mmWave)
systems, where, to minimize the hardware complexity and power consumption, an
analog transmit beamforming and receive combining structure with only one radio
frequency (RF) chain at the base station (BS) and mobile station (MS) is
employed. Most existing works for mmWave channel estimation exploit sparse
scattering characteristics of the channel. In addition to sparsity, mmWave
channels may exhibit angular spreads over the angle of arrival (AoA), angle of
departure (AoD), and elevation domains. In this paper, we show that angular
spreads give rise to a useful low-rank structure that, along with the sparsity,
can be simultaneously utilized to reduce the sample complexity, i.e. the number
of samples needed to successfully recover the mmWave channel. Specifically, to
effectively leverage the joint sparse and low-rank structure, we develop a
two-stage compressed sensing method for mmWave channel estimation, where the
sparse and low-rank properties are respectively utilized in two consecutive
stages, namely, a matrix completion stage and a sparse recovery stage. Our
theoretical analysis reveals that the proposed two-stage scheme can achieve a
lower sample complexity than a direct compressed sensing method that exploits
only the sparse structure of the mmWave channel. Simulation results are
provided to corroborate our theoretical results and to show the superiority of
the proposed two-stage method
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
We consider the problem of recovering block-sparse signals whose structures
are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients
occurring in clusters arise naturally in many practical scenarios. However, the
knowledge of the block structure is usually unavailable in practice. In this
paper, we develop a new sparse Bayesian learning method for recovery of
block-sparse signals with unknown cluster patterns. Specifically, a
pattern-coupled hierarchical Gaussian prior model is introduced to characterize
the statistical dependencies among coefficients, in which a set of
hyperparameters are employed to control the sparsity of signal coefficients.
Unlike the conventional sparse Bayesian learning framework in which each
individual hyperparameter is associated independently with each coefficient, in
this paper, the prior for each coefficient not only involves its own
hyperparameter, but also the hyperparameters of its immediate neighbors. In
doing this way, the sparsity patterns of neighboring coefficients are related
to each other and the hierarchical model has the potential to encourage
structured-sparse solutions. The hyperparameters, along with the sparse signal,
are learned by maximizing their posterior probability via an
expectation-maximization (EM) algorithm. Numerical results show that the
proposed algorithm presents uniform superiority over other existing methods in
a series of experiments
Learning to decompose the modes in few-mode fibers with deep convolutional neural network
We introduce deep learning technique to perform complete mode decomposition
for few-mode optical fiber for the first time. Our goal is to learn a fast and
accurate mapping from near-field beam profiles to the complete mode
coefficients, including both modal amplitudes and phases. We train the
convolutional neural network with simulated beam patterns, and evaluate the
network on both of the simulated beam data and the real beam data. In simulated
beam data testing, the correlation between the reconstructed and the ideal beam
profiles can achieve 0.9993 and 0.995 for 3-mode case and 5-mode case
respectively. While in the real 3-mode beam data testing, the average
correlation is 0.9912 and the mode decomposition can be potentially performed
at 33 Hz frequency on Graphic Processing Unit, indicating real-time processing
ability. The quantitative evaluations demonstrate the superiority of our deep
learning based approach
Deep learning enabled superfast and accurate M^2 evaluation for fiber beams
We introduce deep learning technique to predict the beam propagation factor
M^2 of the laser beams emitting from few-mode fiber for the first time, to the
best of our knowledge. The deep convolutional neural network (CNN) is trained
with paired data of simulated near-field beam patterns and their calculated M^2
value, aiming at learning a fast and accurate mapping from the former to the
latter. The trained deep CNN can then be utilized to evaluate M^2 of the fiber
beams from single beam patterns. The results of simulated testing samples have
shown that our scheme can achieve an averaged prediction error smaller than 2%
even when up to 10 eigenmodes are involved in the fiber. The error becomes
slightly larger when heavy noises are added into the input beam patterns but
still smaller than 2.5%, which further proves the accuracy and robustness of
our method. Furthermore, the M^2 estimation takes only about 5 ms for a
prepared beam pattern with one forward pass, which can be adopted for real-time
M^2 determination with only one supporting Charge-Coupled Device (CCD). The
experimental results further prove the feasibility of our scheme. Moreover, the
method we proposed can be confidently extended to other kinds of beams provided
that adequate training samples are accessible. Deep learning paves the way to
superfast and accurate M^2 evaluation with very low experimental efforts.Comment: 12 pages, 10 figure
Weight Normalization based Quantization for Deep Neural Network Compression
With the development of deep neural networks, the size of network models
becomes larger and larger. Model compression has become an urgent need for
deploying these network models to mobile or embedded devices. Model
quantization is a representative model compression technique. Although a lot of
quantization methods have been proposed, many of them suffer from a high
quantization error caused by a long-tail distribution of network weights. In
this paper, we propose a novel quantization method, called weight normalization
based quantization (WNQ), for model compression. WNQ adopts weight
normalization to avoid the long-tail distribution of network weights and
subsequently reduces the quantization error. Experiments on CIFAR-100 and
ImageNet show that WNQ can outperform other baselines to achieve
state-of-the-art performance.Comment: 10 pages, 5 figure
Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method
The solving of the derivative nonlinear Schrodinger equation (DNLS) has
attracted considerable attention in theoretical analysis and physical
applications. Based on the physics-informed neural network (PINN) which has
been put forward to uncover dynamical behaviors of nonlinear partial different
equation from spatiotemporal data directly, an improved PINN method with
neuron-wise locally adaptive activation function is presented to derive
localized wave solutions of the DNLS in complex space. In order to compare the
performance of above two methods, we reveal the dynamical behaviors and error
analysis for localized wave solutions which include one-rational soliton
solution, genuine rational soliton solutions and rogue wave solution of the
DNLS by employing two methods, also exhibit vivid diagrams and detailed
analysis. The numerical results demonstrate the improved method has faster
convergence and better simulation effect. On the bases of the improved method,
the effects for different numbers of initial points sampled, residual
collocation points sampled, network layers, neurons per hidden layer on the
second order genuine rational soliton solution dynamics of the DNLS are
considered, and the relevant analysis when the locally adaptive activation
function chooses different initial values of scalable parameters are also
exhibited in the simulation of the two-order rogue wave solution
Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics
Artificial neural networks have been successfully incorporated into
variational Monte Carlo method (VMC) to study quantum many-body systems.
However, there have been few systematic studies of exploring quantum many-body
physics using deep neural networks (DNNs), despite of the tremendous success
enjoyed by DNNs in many other areas in recent years. One main challenge of
implementing DNN in VMC is the inefficiency of optimizing such networks with
large number of parameters. We introduce an importance sampling gradient
optimization (ISGO) algorithm, which significantly improves the computational
speed of training DNN in VMC. We design an efficient convolutional DNN
architecture to compute the ground state of a one-dimensional (1D) SU() spin
chain. Our numerical results of the ground-state energies with up to 16 layers
of DNN show excellent agreement with the Bethe-Ansatz exact solution.
Furthermore, we also calculate the loop correlation function using the wave
function obtained. Our work demonstrates the feasibility and advantages of
applying DNNs to numerical quantum many-body calculations.Comment: 8 pages, 7 figure
High-efficiency terahertz spin-decoupled meta-coupler for spoof surface plasmon excitation and beam steering
Spoof surface plasmon (SSP) meta-couplers that efficiently integrate other
diversified functionalities into a single ultrathin device are highly desirable
in the modern microwave and terahertz fields. However, the diversified
functionalities, to the best of our knowledge, have not been applied to
circular polarization meta-couplers because of the spin coupling between the
orthogonal incident waves. In this paper, we propose and numerically
demonstrate a terahertz spin-decoupled bifunctional meta-coupler for SSP
excitation and beam steering. The designed meta-coupler is composed of a
coupling metasurface and a propagating metasurface. The former aims at
realizing anomalous reflection or converting the incident waves into SSP under
the illumination of the left or right circular polarization waves,
respectively, and the latter are used to guide out the excited SSP. The
respective converting efficiency can reach 82% and 70% at 0.3THz for the right
and left circular polarization incident waves. Besides, by appropriately
adjusting the reflection phase distribution, many other diversified
functionalities can also be integrated into the meta-coupler. Our study may
open up new routes for polarization-related SSP couplers, detectors, and other
practical terahertz devices
Far-field subwavelength resolution imaging by spatial spectrum sampling
Imaging below the diffraction limit is always a public interest because of
the restricted resolution of conventional imaging systems. To beat the limit,
evanescent harmonics decaying in space must participate in the imaging process.
Here, we introduce the method of spatial spectrum sampling, a novel far-field
superresolution imaging method for microwave and terahertz regime. Strong
dispersion and momentum conservation allow the spoof surface plasmon polaritons
(SSP) structure to become a sensitive probe for spatial harmonics. This enables
that the spatial information of the targets including both propagating and
evanescent components, can be extracted by tuning and recording SSP in the far
field. Then, the subwavelength resolution is constructed by the inversed
Fourier transform of the sampled spatial spectrum. Using the modified
subwavelength metallic grating as the spoof plasmonic structure, a far-field
resolution of 0.17 wavelength is numerically and experimentally verified, and
two-dimensional imaging ability is also fully discussed. The imaging ability
and flexibility can be further optimizing the SSP structures. We are confident
that our working mechanism will have great potentials in the superresolution
imaging applications in the microwave and terahertz frequency rangeComment: 7 figures,48 reference,24 page
Theoretical study of kinetics of proton coupled electron transfer in photocatalysis
Photocatalysis induced by sunlight is one of the most promising approach to
environmental protection, solar energy conversion and sustainable production of
fuels. The computational modeling of photocatalysis is a rapidly expending
field which requires to adapt and further develop the available theoretical
tools. The coupled transfer of proton and electron is an important reaction
during photocatalysis. In this work, we present the first step of our
methodology development in which we apply existing kinetic theory of such
coupled transfer to a model system, namely, methanol photo-dissociation on
rutile TiO(110) surface, with the help of high-level first-principles
calculations. Moreover, we adapt the Stuchebrukhov-Hammes-Schiffer kinetic
theory, where we use the Georgievskii-Stuchebrukhova vibronic coupling, to
calculate the rate constant of the proton coupled electron transfer reaction
for a particular pathway. In particular, we propose a modified expression to
calculate the rate constant which enforces the near-resonance condition for the
vibrational wavefunction during proton tunneling
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