Search results for "Markov"
showing 10 items of 628 documents
Remarks on IEEE 802.11 DCF performance analysis
2005
This letter presents a new approach to evaluate the throughput/delay performance of the 802.11 distributed coordination function (DCF). Our approach relies on elementary conditional probability arguments rather than bidimensional Markov chains (as proposed in previous models) and can be easily extended to account for backoff operation more general than DCF's one.
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Recycling Gibbs sampling
2017
Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…
Path Integral approach via Laplace’s method of integration for nonstationary response of nonlinear systems
2019
In this paper the nonstationary response of a class of nonlinear systems subject to broad-band stochastic excitations is examined. A version of the Path Integral (PI) approach is developed for determining the evolution of the response probability density function (PDF). Specifically, the PI approach, utilized for evaluating the response PDF in short time steps based on the Chapman–Kolmogorov equation, is here employed in conjunction with the Laplace’s method of integration. In this manner, an approximate analytical solution of the integral involved in this equation is obtained, thus circumventing the repetitive integrations generally required in the conventional numerical implementation of …
Algorithmic Aspects of Speech Recognition: A Synopsis
2000
Speech recognition is an area with a sizable literature, but there is little discussion of the topic within the computer science algorithms community. Since many of the problems arising in speech recognition are well suited for algorithmic studies, we present them in terms familiar to algorithm designers. Such cross fertilization can breed fresh insights from new perspectives. This material is abstracted from A. L. Buchsbaum and R. Giancarlo, Algorithmic Aspects of Speech Recognition: An Introduction, ACM Journal of Experimental Algorithmics, Vol. 2, 1997, http://www.jea.acm.org.
Continuous energy-efficient monitoring model for mobile ad hoc networks
2021
The monitoring of mobile ad hoc networks is an observation task that consists of analysing the operational status of these networks while evaluating their functionalities. In order to allow the whole network and applications to work properly, the monitoring task has become of considerable importance. It must be carried out in real-time by performing measurements, logs, configurations, etc. However, achieving continuous energy-efficient monitoring in mobile wireless networks is very challenging considering the environment features as well as the unpredictable behavior of the participating nodes. This paper outlines the challenges of continuous energy-efficient monitoring over mobile ad hoc n…
MDP-based Resource Allocation for Uplink Grant-free Transmissions in 5G New Radio
2020
The diversity of application scenarios in 5G mobile communication networks calls for innovative initial access schemes beyond traditional grant-based approaches. As a novel concept for facilitating small packet transmission and achieving ultra-low latency, grant-free communication is attracting lots of interests in the research community and standardization bodies. However, when a network consists of both grant based and grant-free based end devices, how to allocate slot resources properly between these two categories of devices remains as an unanswered question. In this paper, we propose a Markov decision process based scheme which dynamically allocates grant-free resources based on a spec…
A Bayesian-optimal principle for learner-friendly adaptation in learning games
2010
Abstract Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predic…
Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks
2019
Massive machine-type communication (mMTC) is expected to play a pivotal role in emerging 5G networks. Considering the dense deployment of small cells and the existence of heterogeneous cells, an MTC device can discover multiple cells for association. Under traditional cell association mechanisms, MTC devices are typically associated with an eNodeB with highest signal strength. However, the selected eNodeB may not be able to handle mMTC requests due to network congestion and overload. Therefore, reliable cell association would provide a smarter solution to facilitate mMTC connections. To enable such a solution, a hidden Markov model (HMM) based machine learning (ML) technique is proposed in …
Automatic place detection and localization in autonomous robotics
2007
This paper presents an approach for the simultaneous learning and recognition of places applied to autonomous robotics. While noteworthy results have been achieved with respect to off-line training process for appearance-based navigation, novel issues arise when recognition and learning are simultaneous and unsupervised processes. The approach adopted here uses a Gaussian mixture model estimated by a novel incremental MML-EM to model the probability distribution of features extracted by image-preprocessing. A place detector decides which features belong to which place integrating odometric information and a hidden Markov model. Tests demonstrate that the proposed system performs as well as …