31,823 research outputs found
Probabilistic Estimation of Chirp Instantaneous Frequency Using Gaussian Processes
We present a probabilistic approach for estimating chirp signal and its
instantaneous frequency function when the true forms of the chirp and
instantaneous frequency are unknown. To do so, we represent them by joint
cascading Gaussian processes governed by a non-linear stochastic differential
equation, and estimate their posterior distribution by using stochastic filters
and smoothers. The model parameters are determined via maximum likelihood
estimation. Theoretical results show that the estimation method has a bounded
mean squared error. Experiments show that the method outperforms a number of
baseline methods on a synthetic model, and we also apply the method to analyse
a gravitational wave data.Comment: Submitted to IEEE Transactions on Signal Processin
Minding impacting events in a model of stochastic variance
We introduce a generalisation of the well-known ARCH process, widely used for
generating uncorrelated stochastic time series with long-term non-Gaussian
distributions and long-lasting correlations in the (instantaneous) standard
deviation exhibiting a clustering profile. Specifically, inspired by the fact
that in a variety of systems impacting events are hardly forgot, we split the
process into two different regimes: a first one for regular periods where the
average volatility of the fluctuations within a certain period of time is below
a certain threshold and another one when the local standard deviation
outnumbers it. In the former situation we use standard rules for
heteroscedastic processes whereas in the latter case the system starts
recalling past values that surpassed the threshold. Our results show that for
appropriate parameter values the model is able to provide fat tailed
probability density functions and strong persistence of the instantaneous
variance characterised by large values of the Hurst exponent is greater than
0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on
SLO-aware Colocation of Data Center Tasks Based on Instantaneous Processor Requirements
In a cloud data center, a single physical machine simultaneously executes
dozens of highly heterogeneous tasks. Such colocation results in more efficient
utilization of machines, but, when tasks' requirements exceed available
resources, some of the tasks might be throttled down or preempted. We analyze
version 2.1 of the Google cluster trace that shows short-term (1 second) task
CPU usage. Contrary to the assumptions taken by many theoretical studies, we
demonstrate that the empirical distributions do not follow any single
distribution. However, high percentiles of the total processor usage (summed
over at least 10 tasks) can be reasonably estimated by the Gaussian
distribution. We use this result for a probabilistic fit test, called the
Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms.
To check whether a new task will fit into a machine, GPA checks whether the
resulting distribution's percentile corresponding to the requested service
level objective, SLO is still below the machine's capacity. In our simulation
experiments, GPA resulted in colocations exceeding the machines' capacity with
a frequency similar to the requested SLO.Comment: Author's version of a paper published in ACM SoCC'1
A Fourier transform method for nonparametric estimation of multivariate volatility
We provide a nonparametric method for the computation of instantaneous
multivariate volatility for continuous semi-martingales, which is based on
Fourier analysis. The co-volatility is reconstructed as a stochastic function
of time by establishing a connection between the Fourier transform of the
prices process and the Fourier transform of the co-volatility process. A
nonparametric estimator is derived given a discrete unevenly spaced and
asynchronously sampled observations of the asset price processes. The
asymptotic properties of the random estimator are studied: namely, consistency
in probability uniformly in time and convergence in law to a mixture of
Gaussian distributions.Comment: Published in at http://dx.doi.org/10.1214/08-AOS633 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Frequency and Phase Synchronization in Stochastic Systems
The phenomenon of frequency and phase synchronization in stochastic systems
requires a revision of concepts originally phrased in the context of purely
deterministic systems. Various definitions of an instantaneous phase are
presented and compared with each other with special attention payed to their
robustness with respect to noise. We review the results of an analytic approach
describing noise-induced phase synchronization in a thermal two-state system.
In this context exact expressions for the mean frequency and the phase
diffusivity are obtained that together determine the average length of locking
episodes. A recently proposed method to quantify frequency synchronization in
noisy potential systems is presented and exemplified by applying it to the
periodically driven noisy harmonic oscillator. Since this method is based on a
threshold crossing rate pioneered by S.O. Rice the related phase velocity is
termed Rice frequency. Finally, we discuss the relation between the phenomenon
of stochastic resonance and noise-enhanced phase coherence by applying the
developed concepts to the periodically driven bistable Kramers oscillator.Comment: to appear in the Chaos focus issue on "Control, communication, and
synchronization in chaotic dynamical systems
Delay Performance of MISO Wireless Communications
Ultra-reliable, low latency communications (URLLC) are currently attracting
significant attention due to the emergence of mission-critical applications and
device-centric communication. URLLC will entail a fundamental paradigm shift
from throughput-oriented system design towards holistic designs for guaranteed
and reliable end-to-end latency. A deep understanding of the delay performance
of wireless networks is essential for efficient URLLC systems. In this paper,
we investigate the network layer performance of multiple-input, single-output
(MISO) systems under statistical delay constraints. We provide closed-form
expressions for MISO diversity-oriented service process and derive
probabilistic delay bounds using tools from stochastic network calculus. In
particular, we analyze transmit beamforming with perfect and imperfect channel
knowledge and compare it with orthogonal space-time codes and antenna
selection. The effect of transmit power, number of antennas, and finite
blocklength channel coding on the delay distribution is also investigated. Our
higher layer performance results reveal key insights of MISO channels and
provide useful guidelines for the design of ultra-reliable communication
systems that can guarantee the stringent URLLC latency requirements.Comment: This work has been submitted to the IEEE for possible publication.
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