16,445 research outputs found
On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time
Random Access Channel (RACH) has been identified as one of the major
bottlenecks for accommodating massive number of machine-to-machine (M2M) users
in LTE networks, especially for the case of burst arrival of connection
requests. As a consequence, the burst resolution problem has sparked a large
number of works in the area, analyzing and optimizing the average performance
of RACH. However, the understanding of what are the probabilistic performance
limits of RACH is still missing. To address this limitation, in the paper, we
investigate the reliability of RACH with access class barring (ACB). We model
RACH as a queuing system, and apply stochastic network calculus to derive
probabilistic performance bounds for burst resolution time, i.e., the worst
case time it takes to connect a burst of M2M devices to the base station. We
illustrate the accuracy of the proposed methodology and its potential
applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC),
201
Wavelet domain Bayesian denoising of string signal in the cosmic microwave background
An algorithm is proposed for denoising the signal induced by cosmic strings
in the cosmic microwave background (CMB). A Bayesian approach is taken, based
on modeling the string signal in the wavelet domain with generalized Gaussian
distributions. Good performance of the algorithm is demonstrated by simulated
experiments at arcminute resolution under noise conditions including primary
and secondary CMB anisotropies, as well as instrumental noise.Comment: 16 pages, 11 figures. Version 2 matches version accepted for
publication in MNRAS. Changes include substantial clarifications on our
approach and a significant reduction of manuscript lengt
Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis
In performing a Bayesian analysis of astronomical data, two difficult
problems often emerge. First, in estimating the parameters of some model for
the data, the resulting posterior distribution may be multimodal or exhibit
pronounced (curving) degeneracies, which can cause problems for traditional
MCMC sampling methods. Second, in selecting between a set of competing models,
calculation of the Bayesian evidence for each model is computationally
expensive. The nested sampling method introduced by Skilling (2004), has
greatly reduced the computational expense of calculating evidences and also
produces posterior inferences as a by-product. This method has been applied
successfully in cosmological applications by Mukherjee et al. (2006), but their
implementation was efficient only for unimodal distributions without pronounced
degeneracies. Shaw et al. (2007), recently introduced a clustered nested
sampling method which is significantly more efficient in sampling from
multimodal posteriors and also determines the expectation and variance of the
final evidence from a single run of the algorithm, hence providing a further
increase in efficiency. In this paper, we build on the work of Shaw et al. and
present three new methods for sampling and evidence evaluation from
distributions that may contain multiple modes and significant degeneracies; we
also present an even more efficient technique for estimating the uncertainty on
the evaluated evidence. These methods lead to a further substantial improvement
in sampling efficiency and robustness, and are applied to toy problems to
demonstrate the accuracy and economy of the evidence calculation and parameter
estimation. Finally, we discuss the use of these methods in performing Bayesian
object detection in astronomical datasets.Comment: 14 pages, 11 figures, submitted to MNRAS, some major additions to the
previous version in response to the referee's comment
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