Locally Optimal Handover Algorithm Final

7/30/2019 Locally Optimal Handover Algorithm Final

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Universitt BremenLocally Optimal Handover Algorithms

for Wireless CommunicationsBremen, 23.06.05

Dipl.-Ing. Stefan Aust

Locally Optimal Handover Algorithmsfor Wireless Communications

Theory Seminar (TS05)


ComNets, University of Bremen


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Overview

Motivation

Locally optimal handover algorithm

Performance analysis of hard handoff algorithm Adaptive hard handoff algorithm

Locally optimal soft handoff algorithm

Conclusion


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Motivation

Handover design for cellular communication networksbased on signal strength measurements

Find an algorithm that is optimal for handover decisions Analytical approach, not useful in practice

Optimal trade-off between system parameters (number of

service failures and number of handovers) Use a locally optimal handover algorithm (practical

approach)

Adapting the handover algorithm to changingenvironments


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Scenario and Assumptions

A possible mobile trajectory [VK97]

Mobile is moving betweenneighbouring cells

Focus on handover algorithms

based on signal-strengthmeasurements The only reliable

measurement that

can be used Other measurements

(BER, PER) are functionsof the signal strength

Limitations Inter cell handovers (horizontal handovers) 2 parameters (call quality, number of handovers)

1 interface (cellular)


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Criteria and Conflicts

Criteria for efficient handovers for optimal design

Measurement of the call quality given by SNR

Number of handovers

Number of unnecessary handovers

Number of bad handovers

Delay of handover

Only the first two criteria are considered in the study

Conflict of criteria

Keeping the call quality Reducing the number of handovers


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Standard Approach Hysteresis Algorithms

Signal strength hystersis can be used for handoverdecision

Hyseresis effects the trade-off between call quality(signal strength) and number of handovers

A hysteresis level defines when the signal quality issufficient to force the handover

Drawback Unnecessary handovers in areas where signals from

both stations are strong

Definition of the hysteresis height

hXX k

k

k

ck B

k

U

U

B

k ++

=

=

+)(

1

0

1

)(

1 hysteresis level h


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New Approach Optimal Algorithm

Measurement of the call quality (signal strength)

Introducing a threshold that is called service failure (SF)

SNR> SF -> call quality is sufficient

SNR< SF -> call quality is stale (handover should be forced)

Best trade-off between expected number of servicefailures and expected number of handovers

Solution of the optimal algorithm leads to a Bayesformulation

It will lead to a complicated and non-stationary solution

It requires prior knowledge of the mobiles trajectory(location problem)

A locally optimal algorithm can be derived from that


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Bayes Formula

Classical probability

Bayes formula expresses the conditional probability of

an event A occurring, given that the event Bhasoccurred (P(A|B))

Law of total probability)(

)|()(

)(

)()|(

BP

ABPAP

BP

BAPBAP

=

=

)|()()(

1

i

n

i

i ABPAPBP ==

0)( BP

eventspossilbeallofNumber

AforfiteventsofNumberAP

____

_____)( =


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Bayes Framework

Bayesian paradigm supplies a direct solution for nonstandard problems for which there is no naturalclassical approach

A Bayesian defines a model, selects a prior, collectsdata, computes the posterior, and then makespredictions

Various types of Bayesan analyses, standard,informative, hierarchical, continuing, and parametric

Two Bayes operations

The construction of a prior distribution

Updating of the prior distribution to form the posteriordistribution


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Preliminaries of Optimal Algorithm

Two base stations B(1) and B(2) involved in thehandover

Distance d(i) between the mobile and the base station

B(i), where i= 1,2

Measured signal strength has three components

Path loss

Large-scale fluctuations (shadow fading)

Small-scale fluctuations (multipath fading)

Multipath fading is not considered (average, low-passfilter) to avoid unnecessary handovers

No termination (hang-up) probability is considered


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Problem Formulation (2/2)

Handover value Uk Handover when

No handover when

Handover decision

Minimum signal strength for satisfactory service

Service failures

Handovers

c

kk

BB =+1

1=k

U

kk BB =+10=kU

)( kkk IU =

then

then

{ }=


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Optimal Algorithm (1/2)

Optimal policy is a set of all decision functions ,whichprovides the best trade-off E[NSF] and E[NH]

Bayes solution of the optimal handover

Optimum trade-off curve (E[NSF] and E[NH]) can befound by solving the Bayes problem for various values ofc

Parameter ccan be interpreted as the relative cost ofhandovers versus service failures

Parameter ccan be adapted to changing environments

)...( 121 +++= n

][][min SFH NENEc +

Trade-off parameter c>0


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Optimal Algorithm (2/2)

Optimum decision function

Equation can be solved numerically to find the optimalhandover policy using dynamic programming (DP)

Drawbacks Quite complex

Non-stationary

Need for prior knowledge of the trajectory of the mobile Theoretical benchmark in comparison of suboptimal

algorithms

{ } { })( 1)()( 1)( 1111

01

11 ||

=

=

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Locally Optimal Handover Algorithm Final