33,064 research outputs found
Adaptive Differential Feedback in Time-Varying Multiuser MIMO Channels
In the context of a time-varying multiuser multiple-input-multiple-output
(MIMO) system, we design recursive least squares based adaptive predictors and
differential quantizers to minimize the sum mean squared error of the overall
system. Using the fact that the scalar entries of the left singular matrix of a
Gaussian MIMO channel becomes almost Gaussian distributed even for a small
number of transmit antennas, we perform adaptive differential quantization of
the relevant singular matrix entries. Compared to the algorithms in the
existing differential feedback literature, our proposed quantizer provides
three advantages: first, the controller parameters are flexible enough to adapt
themselves to different vehicle speeds; second, the model is backward adaptive
i.e., the base station and receiver can agree upon the predictor and variance
estimator coefficients without explicit exchange of the parameters; third, it
can accurately model the system even when the correlation between two
successive channel samples becomes as low as 0.05. Our simulation results show
that our proposed method can reduce the required feedback by several kilobits
per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h
(singular vector tracker). The proposed system also outperforms a fixed
quantizer, with same feedback overhead, in terms of bit error rate up to 30
km/h.Comment: IEEE 22nd International Conference on Personal, Indoor and Mobile
Radio Communications (2011
Shifting Regret, Mirror Descent, and Matrices
We consider the problem of online prediction in changing environments. In this framework the performance of a predictor is evaluated as the loss relative to an arbitrarily changing predictor, whose individual components come from a base class of predictors. Typical results in the literature consider different base classes (experts, linear predictors on the simplex, etc.) separately. Introducing an arbitrary mapping inside the mirror decent algorithm, we provide a framework that unifies and extends existing results. As an example, we prove new shifting regret bounds for matrix prediction problems
Impact of time-variant turbulence behavior on prediction for adaptive optics systems
For high contrast imaging systems, the time delay is one of the major
limiting factors for the performance of the extreme adaptive optics (AO)
sub-system and, in turn, the final contrast. The time delay is due to the
finite time needed to measure the incoming disturbance and then apply the
correction. By predicting the behavior of the atmospheric disturbance over the
time delay we can in principle achieve a better AO performance. Atmospheric
turbulence parameters which determine the wavefront phase fluctuations have
time-varying behavior. We present a stochastic model for wind speed and model
time-variant atmospheric turbulence effects using varying wind speed. We test a
low-order, data-driven predictor, the linear minimum mean square error
predictor, for a near-infrared AO system under varying conditions. Our results
show varying wind can have a significant impact on the performance of wavefront
prediction, preventing it from reaching optimal performance. The impact depends
on the strength of the wind fluctuations with the greatest loss in expected
performance being for high wind speeds.Comment: 10 pages, 8 figures; Accepted to JOSA A March 201
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
We present a machine learning-based methodology capable of providing
real-time ("nowcast") and forecast estimates of influenza activity in the US by
leveraging data from multiple data sources including: Google searches, Twitter
microblogs, nearly real-time hospital visit records, and data from a
participatory surveillance system. Our main contribution consists of combining
multiple influenza-like illnesses (ILI) activity estimates, generated
independently with each data source, into a single prediction of ILI utilizing
machine learning ensemble approaches. Our methodology exploits the information
in each data source and produces accurate weekly ILI predictions for up to four
weeks ahead of the release of CDC's ILI reports. We evaluate the predictive
ability of our ensemble approach during the 2013-2014 (retrospective) and
2014-2015 (live) flu seasons for each of the four weekly time horizons. Our
ensemble approach demonstrates several advantages: (1) our ensemble method's
predictions outperform every prediction using each data source independently,
(2) our methodology can produce predictions one week ahead of GFT's real-time
estimates with comparable accuracy, and (3) our two and three week forecast
estimates have comparable accuracy to real-time predictions using an
autoregressive model. Moreover, our results show that considerable insight is
gained from incorporating disparate data streams, in the form of social media
and crowd sourced data, into influenza predictions in all time horizon
On neural networks in identification and control of dynamic systems
This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts
Forecasting Time Series with VARMA Recursions on Graphs
Graph-based techniques emerged as a choice to deal with the dimensionality
issues in modeling multivariate time series. However, there is yet no complete
understanding of how the underlying structure could be exploited to ease this
task. This work provides contributions in this direction by considering the
forecasting of a process evolving over a graph. We make use of the
(approximate) time-vertex stationarity assumption, i.e., timevarying graph
signals whose first and second order statistical moments are invariant over
time and correlated to a known graph topology. The latter is combined with VAR
and VARMA models to tackle the dimensionality issues present in predicting the
temporal evolution of multivariate time series. We find out that by projecting
the data to the graph spectral domain: (i) the multivariate model estimation
reduces to that of fitting a number of uncorrelated univariate ARMA models and
(ii) an optimal low-rank data representation can be exploited so as to further
reduce the estimation costs. In the case that the multivariate process can be
observed at a subset of nodes, the proposed models extend naturally to Kalman
filtering on graphs allowing for optimal tracking. Numerical experiments with
both synthetic and real data validate the proposed approach and highlight its
benefits over state-of-the-art alternatives.Comment: submitted to the IEEE Transactions on Signal Processin
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