performance analysis of some resource allocation methods in wireless cellular networks

122 Pages·2013·1.02 MB·English

PERFORMANCE ANALYSIS OF
SOME RESOURCE ALLOCATION METHODS
IN WIRELESS CELLULAR NETWORKS
Ph.D. Dissertation
by
Nam Hoai Do
Department of Networked Systems and Services
Faculty of Electrical Engineering and Informatics
Budapest University of Technology and Economics
Research Supervisor:
Prof. Tien Van Do
Budapest University of Technology and Economics
Budapest, Hungary
2013

Alul´ırott Do Hoai Nam kijelentem, hogy ezt a doktori ´ertekez´est magam
k´esz´ıtettem ´es abban csak a megadott forra´sokat haszna´ltam fel. Minden olyan
r´eszt,amelyetszo´szerint,vagyazonostartalomban,dea´tfogalmazvam´asforr´asbo´l
a´tvettem, egy´ertelmu˝en, a forra´s megad´asa´val megjelo¨ltem.
I, the undersigned Nam Hoai Do hereby declare, that this Ph.D. disserta-
tion was made by myself, and I only used the sources given at the end. Every
part that was quoted word-for-word, or was taken over with the same content, I
noted explicitly by giving the reference of the source.
Az ´ert´ekez´es b´ıra´latai ´es a v´ed´esr˝ol k´eszu¨lt jegyzo˝k¨onyv a Budapesti Mu˝szaki
´esGazdasa´gtudom´anyiEgyetemVillamosm´ern¨oki´esInformatikaiKar´anakD´eka´ni
hivatal´aban el´erhet˝oek.
Thereviewsofthedissertationandthereportofthethesisdiscussionareavail-
able at the Deans Office of the Faculty of Electrical Engineering and Informatics
of the Budapest University of Technology and Economics.
Budapest, May 16, 2013
Nam Hoai Do

Abstract
The analytical modeling forms a very important field of the performance eval-
uation, characterized by much higher computational efficiency over stochastic
discrete event simulation. As a general rule, performance computations from
analytical models (exact or approximate) augmented and verified by simulation
results can give rise to reliable performance studies which can be a strong basis
for reliable design and dependable operation of ICT systems. This dissertation
deals with some issues arising in telecommunications networks using the analyti-
cal performance modeling with application of Quasi Birth-Death process and its
variants.
In Chapter 2, some aspects of High Altitude Platform (HAP) based High
Speed Downlink Packet Access (HSDPA) networks are investigated using a vari-
ant of the Sigma queueing model. The model is an integrated one with respect
to HSDPA, capable of accommodating many of the intricate aspects of HSDPA
such as, channel allocation policy, loss of packets due to channel fading, bursty
and correlated traffic. Good agreement is observed between the numerical results
of the proposed analytical model and those of an independent simulator of real
HSDPA and radio channel behaviors of HAP.
In Chapter 3, we proposed an analytical model with Markovian time dura-
tions to investigate an application of radio spectrum renting in mobile cellular
networks. We simultaneously integrated the aspects of renting policy involving
a block of user channels and call admission control in wireless cellular networks
in one analytical model. We taken into account that a rented frequency band
accommodates the number of user channels. An operation rule based on a hys-
teresis control with two thresholds is proposed for the network operator to rent or
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give back frequency bands based on the offered traffic. Furthermore, several vari-
antsoftheFractionalGuardChannelPolicywereincorporatedinourmodel. The
comparison between the proposed queueing model and a simulation confirms that
the queueing model incorporating exponentially distributed call durations can be
used to evaluate the performance of mobile cellular networks with call holding
times following the lognormal distribution as well. Numerical results show that
only the spectrum renting can be used to decrease the blocking probability of
fresh calls without compromising the GoS of handover calls.
In Chapter 4, we enhanced the HM2 algorithm of Domenech-Benlloch et al.,
that enable the approximation of the performance of a multiserver retrial queue
with impatient customers. We derived exact expressions for the computation of
the rate matrix and the conditional mean number of customers. We explored the
behavior of performance measures versus N, then the estimation of threshold N
is derived. We have presented some important properties and provided a proof of
concerning the determination of important quantities. Based on the derivations,
we have constructed an efficient algorithm for the stationary distribution with
the determination of a threshold that allows the computation of performance
measures with a specific accuracy. We have shown that the computational time
complexityofouralgorithmisofO(c)andhasthesameaccuracyasoftheoriginal
HM2 algorithm.
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Table of Contents
Abstract v
List of Figures v
List of Tables vii
Acknowledgements ix
1 Introduction 1
1.1 Motivations and purposes . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 The organisation of this thesis . . . . . . . . . . . . . . . . . . . . 3
2 Analytical Model of HAP-based HSDPA Networks 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Technical Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 High Speed Downlink Packet Access (HSDPA) . . . . . . . 10
2.2.2 High Altitude Platforms (HAPs) . . . . . . . . . . . . . . 13
2.3 The Queuing Model . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 The arrival process . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Varying number of servers and service parameters . . . . . 16
2.3.3 Negative customers . . . . . . . . . . . . . . . . . . . . . . 17
2.3.4 Markov chain representation of the queue . . . . . . . . . . 18
2.3.5 Batch departures . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.6 Condition for stability . . . . . . . . . . . . . . . . . . . . 20
2.3.7 The steady state balance equations . . . . . . . . . . . . . 20
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TABLE OF CONTENTS
2.4 Performance evaluation of UEs in HAP environments . . . . . . . 24
2.4.1 An integrated analytical performance model . . . . . . . . 24
2.4.2 Fading channel behavior . . . . . . . . . . . . . . . . . . . 27
2.4.3 A Numerical Study . . . . . . . . . . . . . . . . . . . . . . 30
2.4.3.1 Validation with a simulation . . . . . . . . . . . . 30
2.4.3.2 Impact of an elevation of HAP . . . . . . . . . . 32
3 Spectrum Renting in Mobile Cellular Networks 39
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2 Technical Background . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.1 Block of User Channels . . . . . . . . . . . . . . . . . . . . 41
3.2.2 An Environment for Spectrum Renting . . . . . . . . . . . 42
3.2.3 Call Admission Control . . . . . . . . . . . . . . . . . . . . 42
3.3 Modeling Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.1 Call Admission Policy . . . . . . . . . . . . . . . . . . . . 44
3.3.2 Spectrum Renting Policy . . . . . . . . . . . . . . . . . . . 44
3.4 A Queueing Model . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.5.1 Comparison with Simulation . . . . . . . . . . . . . . . . . 49
3.5.2 Impacts of Spectrum Renting Policy . . . . . . . . . . . . 49
4 An Enhanced Algorithm for Retrial Queues 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2 A Retrial Queueing Model with Impatient Customers . . . . . . . 59
4.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2.2 An Approximation . . . . . . . . . . . . . . . . . . . . . . 62
4.3 An Enhanced Algorithm . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.1 The computation of matrix R and M(N) . . . . . . . . . . 65
4.3.2 The computation of the steady-state probabilities . . . . . 69
4.3.3 Performance Measures . . . . . . . . . . . . . . . . . . . . 72
4.3.4 An Estimation of N . . . . . . . . . . . . . . . . . . . . . 76
4.3.5 The Convergence Criterion of a Proposed Algorithm . . . 78
4.4 The computational time complexity . . . . . . . . . . . . . . . . . 84
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
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TABLE OF CONTENTS
5 Summary 89
Own publications related to this thesis 93
Other own publications 95
Bibliography 97
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