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Wireless Link Quality Modelling and Mobility Management Optimisation for Cellular Networks PhD Thesis Defence Van Minh Nguyen Paris, June 20 th 2011

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Page 1: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Wireless Link Quality Modelling and Mobility

Management Optimisation for Cellular Networks

PhD Thesis Defence

Van Minh Nguyen

Paris, June 20th 2011

Page 2: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 2

Propagation Path loss

Obstacles Shadowing

Multipath and Motions

Fading, Time-varying

Wireless links

Interference

Link quality

expressed in SINR

Resource sharing

Wireless Networking

Mobile cellular network

Best SINR

node association

is fundamental

Mobility Management

is a fundamental

network defining factor

Page 3: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Outline of Contributions

1. Wireless Link and Best Signal Quality Modelling

1. Stochastic Geometry Modelling of Wireless Links (IEEE WiOPT 2010)

2. Heavy-Tail Asymptotics of Wireless Links (EURASIP JWCN 2010)

2. Level Crossing Analysis of Time-varying Wireless Links

1. Asymptotic Excursions above a Small Level (To be published)

2. Crossings of Successive High Levels (To be published)

3. Applications to Mobility Management in Cellular Networks

1. Analytical Model of Handover Measurement with Application to LTE (IEEE ICC 2011)

2. Autonomous Cell Scanning for Small Cell Networks (EURASIP JWCN 2010)

3. Self-optimisation of Neighbour Cell Lists in Macrocellular Networks (IEEE PIMRC’10)

20 June 2011 PhD Thesis Defence - V.M. Nguyen 3

Page 4: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

BEST SIGNAL QUALITY

MODELLING

Presentation of Approach

Network Assumptions

Stochastic Geometry Modelling

Heavy-Tail Asymptotics Modelling

Page 5: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Signal strength received

at y from transmitter i

Approach

20 June 2011 PhD Thesis Defence - V.M. Nguyen 5

yy

y

y

yy

i

i

ij j

ii

PIN

P

PN

PQ

00

Interference to the

signal of transmitter i

SINR received at y

from transmitter i

Thermal noise at the

reception antenna

Total interference received

at y:

yy

y

yy

yy

S

S

i

i

SiS

MIN

M

PIN

PY

00

max

Best SINR received at y

from set of transmitters S

Maximum signal strength received

at y from set S:

j jPI yy

yy jSj

S PM

max

Joint distribution of the total interference I

and the maximum signal strength MS

Derive the distribution of

the best signal quality YS

Page 6: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Assumptions

Basic wireless link

o y R2 −location of receiver, xi R2 −location of transmitter i,

o Ptx−node’s transmission power, {Zi} −fading,

o {mi} −virtual Tx power assumed i.i.d. of df Fm, m := m1

o 1/l(r) = r - for r R+ and > 2 –pathloss function

Interference field as a shot noise

o {xi}: Poisson point process with intensity on R2

o : independently marked Poisson p.p.

o : non-negative real resp. function

o : SN interference

Set of observed nodes

o B R2 : disk of radius RB centred at the receiver, y 0

o S = set of nodes uniformly selected from B with prob [0, 1]

20 June 2011 PhD Thesis Defence - V.M. Nguyen 6

+

+

++

+

+

+

+

+

+

+

y xi

+

+

++

++

+

+

+

+

+

+

+

Page 7: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Stochastic Geometry Modelling

Page 8: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Primary Result

20 June 2011 PhD Thesis Defence - V.M. Nguyen 8

Joint distribution of I and MS

Page 9: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Observations

20 June 2011 PhD Thesis Defence - V.M. Nguyen 9

Distribution of the Maximum Signal Strength MS

Characteristic Function of the Total Interference I

Page 10: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Skeleton of solution finding

for

20 June 2011 PhD Thesis Defence - V.M. Nguyen

+

+

++

++

+

+

+

+

+

+

+ +

+

+

+

+

+

++

+

+ +

=

+

+

o Step 1: decompose into three independent

independently marked Poisson p.p.

o Step 2: apply the Laplace transform of each shot

noise by Prop 2.2.4 in [Baccelli2009]

F. Baccelli and B. Blaszczyszyn. “Stochastic Geometry and

Wireless Networks, Volume I – Theory”. Foundations and

Trends in Networking, vol. 3(3-4), pp.249-449, 2009.

10

Page 11: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 11

Tail Distribution of the Best Signal Quality

Page 12: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 12

Network Assumptions

o Nodes are spatially distributed according to a Poisson point process

o Virtual transmission powers {mi} are i.i.d. with general distribution Fm

o Unbounded power-law pathloss model, 1/l(r) = r - for r R+ and > 2

Main Results

o Joint distribution of I and MS

o Necessary conditions for the integrability & existence of the joint density

o Tail distribution of the best signal quality

Important Observations

o Total interference is a skewed alpha-stable distribution

o Global maximum signal strength is a Fréchet distribution

o Unbounded power-law pathloss introduces very heavy-tailed behaviours

of I and MS

independently of the type of fading

Sto

ch

asti

cG

eom

etr

y

Modellin

g

Page 13: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Heavy-Tail Asymptotics

Page 14: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Overview

20 June 2011 PhD Thesis Defence - V.M. Nguyen 14

Motivation

o Impacts of the pathloss singularity on the tail behaviour of wireless links

Focus

o Unbounded pathloss: 1/l(r) = (max{r, Rmin})- for r R+, > 2, and Rmin = 0

o Bounded pathloss: 1/l(r) = (max{r, Rmin})- for r R+, > 2, and Rmin > 0

o Fading {Zi} are i.i.d. lognormal with parameters (0, Z) with 0 < Z <

o Network area B is bounded with radius RB < .

o (note: with Poisson p.p. assumption of nodes spatial distribution)

Roadmap

o Study the tail equivalent distribution of the signal strength Pi

o Asymptotic joint dist of the total interference & max signal strength

o Tail distribution of the best signal quality

Page 15: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Tail Behaviour of Signal Strength

20 June 2011 PhD Thesis Defence - V.M. Nguyen 15

Interpretation

o The choice of pathloss model has decisive influence on the tail of wireless links

o Decaying power-law path loss is the dominant component

o Under bounded pathloss, the tail of Pi is determined by the lognormal fading

Theorem

Page 16: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Asymptotic Distribution of Max Signal Strength

20 June 2011 PhD Thesis Defence - V.M. Nguyen 16

Interpretation

o Network densification scenario: n within a bounded network area B

o Unbounded pathloss: Mn is asymptotically Fréchet distribution under both

network extension and network densification

o Bounded pathloss: Mn is asymp. Gumbel dist. under network densification

Theorem

Page 17: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Joint Density

Asymptotic Joint Distribution

20 June 2011 PhD Thesis Defence - V.M. Nguyen 17

Asymptotic Independence

Page 18: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 18

Tail Distribution of the Best Signal Quality

Evaluation of

Shannon

capacity using

tail distribution

of the best

signal quality

Page 19: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 19

Focus

o Impacts of the singularity of power-law pathloss on wireless links

o Network densification scenario: n within a bounded network area B

o Fading {Zi} are i.i.d. lognormal with parameters (0, Z) with 0 < Z <

Unbounded pathloss

o Very heavy-tailed behaviours of interference and maximum signal strength

o Interference and maximum signal strength behave dependently due the

common dominant component corresponding to the pathloss singularity

Heavy T

ail A

sym

pto

tics

Modellin

g

Bounded pathloss

o Asymptotic ind. between the interference and the max signal strength

o Approximation of the tail distribution of the best signal quality

Page 20: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

LEVEL CROSSING PROPERTIES OF

A STATIONARY GAUSSIAN PROCESS

Excursions Above a Low Level

Crossings of Successive High Levels

Page 21: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 21

0

+

-

time

What distribution?

Crossing rate?

Length?

X(t) - stationary Gaussian

• zero mean, variance 0

• ACF RX() with finite 2nd

derivative at origin 2

[Leadbetter83]

[Cramèr67] H. Cramèr and M. R. Leadbetter. Stationary and related stochastic processes: Sample function properties and their applications, volume 7. John Wiley and Sons, Inc, 1967.

Exponential distribution of rate ED- [Cramèr67]

1

2

-

[Rice58]

[Rice58] S. O. Rice. “Distribution of the duration of fades in radio transmission: Gaussian noise model”. Bell Syst. Tech. J., 37(3):581-635, 1958

[Leadbetter83] M. R. Leadbetter, G. Lindgren, and H. Rootzen. Extremes and Related Properties of Random Sequences and Processes. Springer Verlag. 1983

Page 22: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Main Result (1/2)

20 June 2011 PhD Thesis Defence - V.M. Nguyen 22

Excursion Above a Very Low Level

Observation

o EU 0 as : P(u ) 0 < , i.e. X(t) above a low level most of the time

o Thus, for an excursion above a low level, we only know the distribution of length

o By contrast, an excursion above a high level is short: length & trajectory by [Leadbetter83]

[Cramèr67]: for the

exponential dist of time

between two successive

down-crossings

By the memorylessness

of exponential dist

Page 23: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Main Result (2/2)

20 June 2011 PhD Thesis Defence - V.M. Nguyen 23

Crossings of Successive High Levels

[Leadbetter83]: for the

asymp. parabola trajectory

of up-excursion above a

high lelvel

Page 24: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

ANALYTICAL MODEL OF

HANDOVER MEASUREMENT

HO Measurement Procedure

Skeleton of Analytical Solution

Application to Long Term Evolution

Page 25: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 25

Handover

Mobile measures neighbouring cells and

reports to the network

Purpose: to find a suitable HO target when

the serving cell’s signal deteriorates

Literature: simulation and parameter-

specific approaches, poor in analytical

Network decides and executes the connection

switching

Purpose: to perform optimal and reliable

connection switching

Literature: very rich including optimal control,

signal prediction, protocol design

Handover Decision-ExecutionHandover Measurement

Our work

Page 26: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Why is Analytical Model for HO Measurement?

20 June 2011 PhD Thesis Defence - V.M. Nguyen 26

Strong impacts of HO Measurement on

o The quality of the handover target

o The user’s experience: service interruptions, throughput degradation

Complex operation of HO Measurement due to

o Specific PHY layer procedures: e.g., frame structure, synchronisation,

o The measurement capability of mobile terminal

o Combining effect of RRC parameters, e.g. > 10 Triggering Events in WDCMA, 7 in LTE

o Time-varying and spatial-varying factors, e.g. signal quality, user’s mobility

o The interference nature of a multiple-cell system

generalised analytical model of handover measurement

is helpful to understand

unified impacts of controlling params + user’s mobility + system capabilities

on the system performance

Page 27: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Generic HO Measurement Procedure

20 June 2011 PhD Thesis Defence - V.M. Nguyen 27

Measurement

procedure is triggered

Scan neighbour cells &

Monitor serving cell’s

signal quality

Serving cell’s

signal is too bad or

becomes good?

A suitable

handover target is

found?

Service

failure

Withdrawal

Target

found

Continue

Scanning

Too bad

Become Good

Yes

Yes

No

No

A primary objective of the network configuration is to minimise the

probability of service failure due to the handover measurement

Page 28: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Basic Probabilistic Events

20 June 2011 PhD Thesis Defence - V.M. Nguyen 28

Service failure level

Triggering level

SINR(t)

tService

Interruption

Tfail

Scan

Ttrig

Withdraw

Twdraw

Scan

Ttrig

Measurement periodduring which themobile is able tomeasure k cells

Page 29: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 29

1 2

34

Scan

CellSwitch

NoScan

Fail

P(Withdrawal)=

P(excur. of X(t) above Xwdraw with

length longer than Twdraw)

Level crossing

P(Target Found)P(Service Failure)=

P(excur. of X(t) below Xfail with

length longer than Tfail)

Level crossing

P(Triggering)=

P (excur. of X(t) below Xtrig with ≥ Ttrig

and

NOT excur. of X(t) below Xfail with ≥ Tfail)

Level crossing of successive levels

Page 30: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 30

1 2

34

Scan

CellSwitch

NoScan

Fail

MS is connected to the current serving cell

MS is NOT connected to the current serving cell

1 2

34

Scan

CellSwitch

NoScan

Fail

Mobile in connected-mode Mobile in HO measurement

Transition matrix M

FD: distribution of call duration within one cell

Measurement Failure:

Measurement Success:

Quality of Target Cell:

Page 31: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Evaluation parameters

LTE Handover Measurement

20 June 2011 PhD Thesis Defence - V.M. Nguyen 31

Intra-freq Measurement

Intra-frequency measurement is

predominant due to frequency-reuse 1 of

LTE

UE measures intra-frequency cells

continuously during RRC_CONNECTED

mode

o P(Scanning Triggering) = 1

o P(Withdrawal) = 0

UE measures intra-frequency cells

autonomously using all 504 physical cell Ids

(PCIs)

o Unlimited candidate set of target cell

Page 32: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

32ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen

Increasing the measurement capability more than about 102 improves the

performance very marginally

Regarding FAILURE probability plot: current LTE requirement for measurement

capability of k = 8 seems insufficient for reliable HO performance

(a) HO measurement SUCCESS probability

Measurement capability, k

(b) HO measurement FAILURE probability

Measurement capability, k

Page 33: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

33

Relative threshold: robust service (i.e. low SINRfail min), enhances target cell’s quality

Absolute threshold: crossing point for k in-between 10 and 16. Set low SINRreq for small

k, and set high SINRreq for big k in order to achieve greater performance

ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen

(b) Target cell’s signal quality

(a) Quality of target cell

under Relative threshold, i.e.

SINRreq = SINRfail + HO

(b) Quality of target cell

under Absolute threshold, i.e.

SINRfail = - 20 dB

Measurement capability, k Measurement capability, k

Page 34: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 34

LTE intra-frequency measurement

o High meas. capability enhances the mobility management performance

o Current requirement of 8 intra-freq cells / 200ms seems insufficient

o Measurement capability higher than 10² cells / 200ms

• Marginal improvement in the HO measurement performance

• Significant enhancement of the quality of target cell

o Future applications to Inter-freq and Inter-RAT measurements

Han

dover

Measu

rem

en

tAnalytical model

o Characterise HO measurement procedure as a Markov chain by determining

key events associated with a discrete-time model

o Formulate and derive key probabilistic events using the developed results on

the best signal quality and on level crossings

Other applications

o Autonomous scanning for small cell networks

o Self-optimisation of neighbour cell lists

Page 35: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Conclusion

20 June 2011 PhD Thesis Defence - V.M. Nguyen 35

Distribution of the Best Signal Quality

o Method: by means of the joint distribution of interference and max signal strength

o Stochastic geometry model: exact expression of the tail distribution

o Heavy-tail asymptotics: an approximation of the tail dsitribution

Level crossing of a stationary Gaussian process

o Length of an excursion above a very low level is exponentially distributed

o Mean number of crossings, length of an excursion of crossings of two successive levels

Mobility management

o Focus on handover measurement function

o Analytical model using developed results on best signal quality and level crossings

o Application to LTE Intra-frequency measurement

Page 36: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

[email protected]

Thank You

Page 37: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

BACKUP

Page 38: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Skeleton of solution finding (cont’d)

20 June 2011 PhD Thesis Defence - V.M. Nguyen 38

=

=

=

F. Baccelli and B. Blaszczyszyn. “Stochastic Geometry and

Wireless Networks, Volume I – Theory”. Foundations and

Trends in Networking, vol. 3(3-4), pp.249-449, 2009.

Apply Laplace transform of additive shot noise

given by Proposition 2.2.4 in [Baccelli2009]

Page 39: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

20 June 2011 PhD Thesis Defence - V.M. Nguyen 39

Autonomous Scanning for Small Cell Networks (EURASIP JWCN 2010)

o Challenge: High-density and randomness of small cell networks require for new logic

in the implementation of standardised mobility management mechanism

o Solution: Propose and optimise autonomous scanning for max data throughput

o Conclusion: Autonomously scan 30 cells is effective for a common network setting

o Tools: best signal quality for network densification scenario

Analytical Model of Handover Measurement (IEEE ICC 2011)

o Challenge: HO Measurement has strong impact on the whole system performance, its

operation is complex while its state-of-the-art is weak

o Solution: Generalised analytical model, then investigation of LTE HO measurement

o Conclusion: Current LTE UE capability seems insufficient for reliable HO performance

o Tools: best signal quality for network extension & densification, level crossings

Self-optimisation of Neighbour Cell Lists (IEEE PIMRC 2010)

o Challenge: Manual configuration of NCLs is a big every-day operator's concern

o Solution: Propose measurement-based auto-configuration & self-optimisation

o Conclusion: Attain 99% of scanning success without incurring signalling overhead

o Tools: self-organisation paradigm

Overv

iew

of

Applicati

on

s

Page 40: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Solution

o [Mandayam98]: result for the case of constant Zfail assuming constant interference I

o We generalised the result for the case of random Zfail taking into account random I

Formulation

Service Failure

ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen 40

N. Mandayam, P.-C. Chen, and J. Holtzman. “Minimum Duration Outage for CDMA Cellular Systems: A Level Crossing Analysis”, Wireless Pers. Commun., Springer Netherlands, 1998, vol.7, pp. 135-146

This solution is similarly applied for the probability of scanning withdrawal

Page 41: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Step 1 – Representation in Basic Events

Scanning Triggering (1/2)

41ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen

Computationally

similar to Service

Failure

P(A * !B) = P(A) – P(A * B)

Page 42: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Step 2 – Application of Level Crossing Results

Scanning Triggering (2/2)

42ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen

Mean number

of crossings of

successive levels

Length of

crossings of

successive levels

Basic of service

failure probability

Page 43: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Formulation

Suitable Target Found

ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen 43

Tail of the Best

Signal Quality

Unlimited candidate set

o No restriction on the set of cells to be measured, e.g. intra-freq in WCDMA and LTE

o Apply the result on the tail distribution of the best signal quality for network

extension scenario (obtained with stochastic geometry model)

Limited candidate set

o The mobile only measures cells belonging to a predefined set, e.g. neighbour cell list

o Apply the result on the tail distribution of the best signal quality for network

densification scenario (obtained with heavy-tail asymptotics)

Page 44: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

44

(a) Tail distribution of Yk(b) Results with level crossing analysis

Analytical results agree with simulation

High measurement capability increases the probability of finding a suitable target

Robust service, i.e. low SINRfail ( min), reduces the service failure probability

ICC 2011 - June 7th 2011 PhD Thesis Defence - V.M. Nguyen

Measurement capability, k Measurement instants, m

Probability of service failureProbability of finding suitable target

Page 45: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Articles

Bibliography

V. M. Nguyen, F. Baccelli, L. Thomas, and C. S. Chen.

Best signal quality in cellular networks: asymptotic properties and applications to mobility management in small

cell networks.

EURASIP J. Wireless Commun. Netw. (JWCN), spec. issue on femtocell networks, pp. 1-14, Mar. 2010.

V. M. Nguyen, C. S. Chen, and L. Thomas.

Handover measurement in mobile cellular networks: analysis and applications to LTE'.

In Proceedings of IEEE International Conference on Communications (ICC) 2011. Japan, June 2011.

V. M. Nguyen and F. Baccelli.

A stochastic geometry model for the best signal quality in a wireless network.

Proceeding of IEEE International Symposium on Modeling and Optimization in Mobile, Ad-Hoc and Wireless

Networks (WiOPT) 2010, pp. 465-471. France, June 2010

V. M. Nguyen and H. Claussen.

Efficient self-optimization of neighbour cell lists in macrocellular networks.

Proceedings of IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC)

2010, pp. 1921-1926. Turkey, Sept. 2010.

V. M. Nguyen and L. Thomas.

Efficient dynamic multi-step paging for cellular wireless networks.

Bell Labs Tech. J., special issue on Core and Wireless Networks, vol.14(2), pp. 203-221. Aug. 2009.

20 June 2011 PhD Thesis Defence - V.M. Nguyen 45

Page 46: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Articles (cont’d)

V. M. Nguyen.

Extreme value modeling of the best signal quality and applications for small cell networks.

Joint workshop of Bell Labs, Fraunhofer HHI, and Deutsche Telekom Labs on The Future of Communications:

Science, Technologies, and Services. Berlin, June 2010.

V. M. Nguyen.

Some properties of level crossings of a stationary process and applications. In preparation.

V. M. Nguyen, C. S. Chen, and L. Thomas.

A unified analytical model of handover measurement for mobile cellular networking.

In preparation for IEEE /ACM Trans. Netw.

20 June 2011 PhD Thesis Defence - V.M. Nguyen 46

Standard Constributions

Alcatel-Lucent/V. M. Nguyen.

Identifying coverage islands.

3GPP LTE Standard Contribution. TSG-RAN WG3\#66, R3-092949. Nov. 2009.

Alcatel-Lucent/V. M. Nguyen.

UE measurements in coverage islands.

3GPP LTE Standard Contribution. TSG-RAN WG3\#66, R3-092950. Nov. 2009.

Alcatel-Lucent/V. M. Nguyen.

Handling of UE measurements and transfer of UE history for mobility robustness optimization.

3GPP LTE Standard Contribution. TSG-RAN WG3\#66, R3-092951. Nov. 2009.

Page 47: Wireless Link Quality Modelling and Mobility Management ... · Presentation of Approach Network Assumptions Stochastic Geometry Modelling Heavy-Tail Asymptotics Modelling. Signal

Patent Applications

V. M. Nguyen and H. Claussen.

Method for automatically configuring a neighbor cell list for a base station in a cellular wireless network.

European Patent Appl. No.08291260.1 (31.12. 08). International Patent Appl, No.PCT/EP2009/009204 (21.12.09)

V. M. Nguyen and O. Marcé.

Adaptive time allocation to reduce impacts of scanning.

European Patent Appl, No.08291265.0 (31.12.08). International Patent Appl. No.PCT/EP2009/009205 (21.12.09)

V. M. Nguyen and Y. El Mghazli.

Method and equipment for dynamically updating neighboring cell lists in heterogenous networks.

European Patent Application, No. 09290135.4 (25.02.09).

V. M. Nguyen and Y. El Mghazli.

Method and apparatus for new cell discovery.

US Patent Appl, No.12/383,907(30.03.09). International Patent Appl, No.PCT/US2010/026496 (08.03.10)

V. M. Nguyen, L. Thomas, and O. Marcé.

Method and controller for paging a mobile set in a cellular network.

European Patent Application, No. 09305029.2 (12.01.09)

O. Marcé, A. Petit, and V. M. Nguyen.

Method for enhancing the handover of a mobile station and base station for carrying out the method.

European Patent Appl, No.09305189.4 (02.03.09). International Patent Appl, No.PCT/EP2010/052425 (25.02.10)

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