442 research outputs found
Time inhomogeneous multivariate Markov chains : detecting and testing multiple structural breaks occurring at unknown
Markov chains models are used in several applications and different areas of study. Usually a Markov chain model is assumed to be homogeneous in the sense that the transition probabilities are time invariant. Yet, ignoring the inhomogeneous nature of a stochastic process by disregarding the presence of structural breaks can lead to misleading conclusions. Several methodologies are currently proposed for detecting structural breaks in a Markov chain, however, these methods have some limitations, namely they can only test directly for the presence of a single structural break. This paper proposes a new methodology for detecting and testing the presence multiple structural breaks in a Markov chain occurring at unknown dates.info:eu-repo/semantics/publishedVersio
Generalized multivariate Markov chains : estimation, inference and implementation in R
Mestrado Bolonha em Econometria Aplicada e PrevisãoEste trabalho propõe uma nova generalização do modelo de Cadeias de Markov Multivariadas. Tipicamente, uma cadeia de Markov é descrita pelos valores passados do pro- cesso, a generalização proposta neste trabalho permiritá também considerar variáveis exó- genas. Especificamente, iremos incorporar os efeitos dos valores passados do processo e os efeitos de variáveis pré-determinadas ou exógenas no modelo. Deste modo, será considerada uma cadeia de Markov não-homogénea em vez de uma cadeia de Markov homogénea. Os resultados da simulação de Monte Carlo mostraram que o modelo pro- posto detectou uma cadeia de Markov não-homogénea e detectou valores específicos dos parâmetros. Porém, quando esses valores eram baixos em magnitude, os resultados da simulação mostraram que o modelo tinha baixo poder de teste. Portanto, para estimativas de baixa magnitude, dever-se-á considerar um nível de significância mais alto ao tes- tar a significância individual dos parâmetros. Adicionalmente, uma ilustração empírica demonstrou a relevância deste novo modelo, ao estimar a matriz de transição de proba- bilidade, para diferentes valores de uma variável exógena. Uma contribuição adicional e prática deste trabalho é o desenvolvimento de uma package R com esta generalização.This essay proposes a new generalization of Multivariate Markov Chains (MMC) model. Typically, a Markov chain is described by the process’ past values, the gener- alization proposed in this work will also consider exogenous variables. Specifically, we will incorporate the effects of the process’ past values and the effects of pre-determined or exogenous covariates in the model. This is achieved by considering a non-homogeneous Markov chain instead of an homogeneous Markov chain. The findings from the Monte Carlo simulation showed that the model proposed detected a non-homogeneous Markov chain and it detected specific values of the parameters. However, when these values were small in magnitude, the results from the simulation showed that the model had low power of test. Hence, for estimates with small magnitude, one should use a higher significance level when testing for individual significance of the parameters. Moreover, an empirical illustration demonstrated the relevance of this new model, by estimating the probabil- ity transition matrix, for different values of the exogenous variable. An additional and practical contribution of this work is the development of a novel R package with this generalization.info:eu-repo/semantics/publishedVersio
Index Modulation-based Information Harvesting for Far-Field RF Power Transfer
While wireless information transmission (WIT) is evolving into its sixth
generation (6G), maintaining terminal operations that rely on limited battery
capacities has become one of the most paramount challenges for
Internet-of-Things (IoT) platforms. In this respect, there exists a growing
interest in energy harvesting technology from ambient resources, and wireless
power transfer (WPT) can be the key solution towards enabling battery-less
infrastructures referred to as zero-power communication technology. Indeed,
eclectic integration approaches between WPT and WIT mechanisms are becoming a
vital necessity to limit the need for replacing batteries. Beyond the
conventional separation between data and power components of the emitted
waveforms, as in simultaneous wireless information and power transfer (SWIPT)
mechanisms, a novel protocol referred to as information harvesting (IH) has
recently emerged. IH leverages existing WPT mechanisms for data communication
by incorporating index modulation (IM) techniques on top of the existing
far-field power transfer mechanism. In this paper, a unified framework for the
IM-based IH mechanisms has been presented where the feasibility of various IM
techniques are evaluated based on different performance metrics. The presented
results demonstrate the substantial potential to enable data communication
within existing far-field WPT systems, particularly in the context of
next-generation IoT wireless networks.Comment: 13 pages, 9 figure
Performance modelling for system-level design
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Channel assembling and resource allocation in multichannel spectrum sharing wireless networks
Submitted in fulfilment of the academic requirements for the degree of
Doctor of Philosophy (Ph.D.) in Engineering, in the School of Electrical and
Information Engineering, Faculty of Engineering and the Built Environment,
at the University of the Witwatersrand, Johannesburg, South Africa, 2017The continuous evolution of wireless communications technologies has increasingly imposed a
burden on the use of radio spectrum. Due to the proliferation of new wireless networks applications
and services, the radio spectrum is getting saturated and becoming a limited resource. To a large
extent, spectrum scarcity may be a result of deficient spectrum allocation and management policies,
rather than of the physical shortage of radio frequencies. The conventional static spectrum
allocation has been found to be ineffective, leading to overcrowding and inefficient use. Cognitive
radio (CR) has therefore emerged as an enabling technology that facilitates dynamic spectrum
access (DSA), with a great potential to address the issue of spectrum scarcity and inefficient use.
However, provisioning of reliable and robust communication with seamless operation in cognitive
radio networks (CRNs) is a challenging task. The underlying challenges include development of
non-intrusive dynamic resource allocation (DRA) and optimization techniques.
The main focus of this thesis is development of adaptive channel assembling (ChA) and DRA
schemes, with the aim to maximize performance of secondary user (SU) nodes in CRNs, without
degrading performance of primary user (PU) nodes in a primary network (PN). The key objectives
are therefore four-fold. Firstly, to optimize ChA and DRA schemes in overlay CRNs. Secondly, to
develop analytical models for quantifying performance of ChA schemes over fading channels in
overlay CRNs. Thirdly, to extend the overlay ChA schemes into hybrid overlay and underlay
architectures, subject to power control and interference mitigation; and finally, to extend the
adaptive ChA and DRA schemes for multiuser multichannel access CRNs.
Performance analysis and evaluation of the developed ChA and DRA is presented, mainly through
extensive simulations and analytical models. Further, the cross validation has been performed
between simulations and analytical results to confirm the accuracy and preciseness of the novel
analytical models developed in this thesis. In general, the presented results demonstrate improved
performance of SU nodes in terms of capacity, collision probability, outage probability and forced
termination probability when employing the adaptive ChA and DRA in CRNs.CK201
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