178 research outputs found
A Traffic Model for Machine-Type Communications Using Spatial Point Processes
A source traffic model for machine-to-machine communications is presented in
this paper. We consider a model in which devices operate in a regular mode
until they are triggered into an alarm mode by an alarm event. The positions of
devices and events are modeled by means of Poisson point processes, where the
generated traffic by a given device depends on its position and event
positions. We first consider the case where devices and events are static and
devices generate traffic according to a Bernoulli process, where we derive the
total rate from the devices at the base station. We then extend the model by
defining a two-state Markov chain for each device, which allows for devices to
stay in alarm mode for a geometrically distributed holding time. The temporal
characteristics of this model are analyzed via the autocovariance function,
where the effect of event density and mean holding time are shown.Comment: Accepted at the 2017 IEEE 28th Annual International Symposium on
Personal, Indoor, and Mobile Radio Communications (PIMRC) - Workshop WS-07 on
"The Internet of Things (IoT), the Road Ahead: Applications, Challenges, and
Solutions
Variational Bayesian Inference of Line Spectra
In this paper, we address the fundamental problem of line spectral estimation
in a Bayesian framework. We target model order and parameter estimation via
variational inference in a probabilistic model in which the frequencies are
continuous-valued, i.e., not restricted to a grid; and the coefficients are
governed by a Bernoulli-Gaussian prior model turning model order selection into
binary sequence detection. Unlike earlier works which retain only point
estimates of the frequencies, we undertake a more complete Bayesian treatment
by estimating the posterior probability density functions (pdfs) of the
frequencies and computing expectations over them. Thus, we additionally capture
and operate with the uncertainty of the frequency estimates. Aiming to maximize
the model evidence, variational optimization provides analytic approximations
of the posterior pdfs and also gives estimates of the additional parameters. We
propose an accurate representation of the pdfs of the frequencies by mixtures
of von Mises pdfs, which yields closed-form expectations. We define the
algorithm VALSE in which the estimates of the pdfs and parameters are
iteratively updated. VALSE is a gridless, convergent method, does not require
parameter tuning, can easily include prior knowledge about the frequencies and
provides approximate posterior pdfs based on which the uncertainty in line
spectral estimation can be quantified. Simulation results show that accounting
for the uncertainty of frequency estimates, rather than computing just point
estimates, significantly improves the performance. The performance of VALSE is
superior to that of state-of-the-art methods and closely approaches the
Cram\'er-Rao bound computed for the true model order.Comment: 15 pages, 8 figures, accepted for publication in IEEE Transactions on
Signal Processin
Superfast Line Spectral Estimation
A number of recent works have proposed to solve the line spectral estimation
problem by applying off-the-grid extensions of sparse estimation techniques.
These methods are preferable over classical line spectral estimation algorithms
because they inherently estimate the model order. However, they all have
computation times which grow at least cubically in the problem size, thus
limiting their practical applicability in cases with large dimensions. To
alleviate this issue, we propose a low-complexity method for line spectral
estimation, which also draws on ideas from sparse estimation. Our method is
based on a Bayesian view of the problem. The signal covariance matrix is shown
to have Toeplitz structure, allowing superfast Toeplitz inversion to be used.
We demonstrate that our method achieves estimation accuracy at least as good as
current methods and that it does so while being orders of magnitudes faster.Comment: 16 pages, 7 figures, accepted for IEEE Transactions on Signal
Processin
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