SOCRATES Final Workshop Matias Toril

8/22/2019 SOCRATES Final Workshop Matias Toril

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1

Automatic Re-planning of Tracking Areas

Matas Toril

Communications Engineering Dept., University of Mlaga, Spain

([email protected])

24/10/2005

Karlsruhe, 22 Feb 2011

FP7 SOCRATES Final Workshop on Self-Organisation in Mobile Networks

(co-located with IWSOS 2011)


2/32

2

Outline

1 The tracking area re-planning problem

2 Gra h-theoretic formulation

3 Solution method

4 Performance analysis

5 Conclusions


3/32

3

Outline

LA

Intro

TA


Location area planning in legacy networks

State of research and technology FOR

2 Graph-theoretic formulation

3 Solution method

SOL

4 Performance analysisANA

onc us ons

CON


4/32 4

The tracking area planning problem

LAIntroTACellular network structuring

PCU

BSC

MSC/SGSN

FOR

LA/RA LA/RAPCU

BSC

LA SOL

PCU PCU PCU PCU

PCU

PCU

PCUANA

BTS

Site Site Site Site Site Site

BTS BTSBTSBTS BTSBTSBTSBTSBTSBTS BTSBTSBTS

LA

CON


5/32 5


LAIntroTALocat on management n current ce u ar networ s

Purpose Know location/state of mobiles and direct mobile terminated calls

Problem Trade-off in location area size Many small LAs more LUs (i.e., DCCH capacity, load in databases)

FOR

. .,

Solutions 1) Alternative LU/paging algorithms

LU (time/distance-based, groupal), paging (selective)

SOL

2) Optimise size/shape of LAs

Minimise total #LUs while keeping # paging messages per LA small

LA #1 LA #2 ANA

BSC MSC[DCCH]

BTS

CN

PG req.PCH

Definition of LAsPaging algorithm

CON

VLR HLR

TMSI/LAC+CIBSSMSLA border


6/32 6


LAIntroTAState of research and technology

Current practice

9 1 LA 1 BSC Many small LAs many mobility LUs large DCCH traf.

e.g., In GERAN, 50% of SDCCH attempts are LUs

FOR

12% of network capacity reserved for SDCCH

9Changes in LA plan only as a result of BSC splitting event

SOL

onstra nt t at s n t e same e ong to t e same

Changes in LA plan lead to temporary congestion of DCCH in affected cells

ANA

BSC BSCBSC

LA/RA LA/RA

BSCBSC

CON

BTS

Site Site Site Site Site Site

PCU PCU PCU PCU

BTS BTSBTSBTSBTS

BTSBTSBTS

BTSBTSBTS

BTSBTS


7/32 7


LAIntroTAa e o researc an ec no ogy

New drivers

9 Chan es in vendor e ui ment LA borders can now cross MSC borders

9 New network algorithms Overlapping TAs [3GPP rel. 7], tracking area list [3GPP rel. 8]

9 Interest on SON NGMN [SONuse cases, O&Mrequirements], 3GPP [Rel. 8/9 LTE]

FOR

e a e wor

9 Graph partitioning Local refinement [Plehn 95], integer programming [Tcha 97],

genetic algorithm [Gondim 96], simulated annealing [Demirkol 04],

SOL

,

9 New network algorithms TA list [Modarres 09] ,TA overlapping [Varsamopoulos 04]

9 Dynamic adaptation Trade-off signalling versus reconfiguration cost [Modarres09]ANA

Main contributions

9 Re-formulation of TA planning as a classical graph partitioning problemCON

Met o to optimise TAs requent y ase on statistics in t e networ management

How often? Which changes? Potential impact on network signalling?


8/32 8

Graph-theoretic formulation

TA1 The tracking area re-planning problem in GERAN


Nave formulation

Adapted advanced formulation

ALGFOR

3 Proposed method

SOL


ANA

CON


9/32 9


TANave formulation

PCU 1 PCU 2LA 1 LA 2

Cell1

1

5 21215

ALGFOR

Cell2

Cell5

14

345 4 23

Network

SOL

Cell3

Cell4

34

Network area optimised:ANA

(TAP) Minimise

subject toOptimisation

9 Currently 1 NMS

CONmo e


10/3210


TAAdapted advanced formulation

1) Paging cost in objective function

2) Paging cost term

paging constraint term ALGFOR

3) Time dependence

LU-to-HOratio

Paging-to-LUcost ratio

'

SOL

(TAP) Minimise1 1( , ) ( ,..., ) ( , ) ( ,..., )

( )k k

ij i j ii j V V i j V V i

r c

+ + + ( )( , )

(1 ) ( )ij ij i j ij iii j

r S S cc + ++ ( ) ( )( , )( , )

( ) 1 ( )ij i j ij i j ii j ii j

r Sc c

+ + +

+

ij

( )( )( , )

( ) 1ij i j iji j E

r Sc

+ ( )( )( ) ( ) ( )( , )

( ) 1s s sij i j iji j E

r Sc

+ ( )( )( ) ( ) ( )( , )

[ ] [ ] [ ] [ ] [ ]( ) 1ij

s s s

i j ij

t i j E

t t t t t r Sc

+ ANA

subject to ( )n

pk

i aw

i VB


11/3211

The assignment of PCUs in GERAN

TA1 The location area re-planning problem in GERAN


3 Solution methodFOR

Classical graph partitioning algorithms

Graph resolution

MODSOL

4 Performance analysis ANA

5 Conclusions

CON


12/3212

Solution method

TAGoals 1) Keep the number of TA re-plans as small as possible

2) Minimise impact of changing the TA plan

-

Proposed methodology

FOR

1) Define time granularity for measurements hour, day, week

2) Collect network stats in several periods

HO, LU, CS traffic, total/peak paging

SOL

3) Build network graphs

4) Compute graph correlation between periods ANA

( ) ( ) ( ), ,ij

s s pi i

( , )u v

5 I enti y corre ate measurement perio s ustering a gorit m e.g., -means

6) Compute TA plan for correlated periods Classical graph partitioning algorithm

in a row from past periods (e.g., ML refinement) CON

7) Select re-configuration instant Low impact on control channels (e.g., night)

8) Estimate users affected by changes (e.g., from traffic distribution)( )c

i


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13

Solution method

TAGraph correlation

Definitions G(2)

FORG(1)[ ] ( , )

[ ]

ij

i

i j E

i V

=

=

SOL

G(0)

| |

[ ] , ,

( ) ( ) ( ) ( )1 E s s s s

i j E i V

u u v v

=

30

0.99

1

ANA

1 ( ) ( )

| |

,| |

( ) ( ) ( ) ( )1( , )

s u v

V

s s s s

E

u u v vu v

=

=

20

25

0.97

0.98

CON

1 ( ) ( )

1( , )

s u v

u v

=

=| | | |

1 ( ) ( )| | | |

( ) ( ) ( ) ( )E Vs s s s

s u vE V

u u v v

+

= +

5

10

15

0.94

0.95

0.96

Graph correlation coefficient

and clusters

5 10 15 20 25 300.93


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14

Solution method

TAClassical graph partitioning algorithms

Refinement algorithms Multi-level refinement

ree y a gor m

9 Kernighan-Lin algorithm (KL)

9 Fiduccia-Mattheyses algorithm (FM)

oarsen ng a gor m

9 Initial partitioning

9 Uncoarsening algorithm

FOR

* Example: k=2, Baw=9 MODSOL

ANA

G(0)

G(1) G(1)

G(0)-3-1-1-3

-2-3-3-2

-1+1-3-3

-2-1-3-2

-1+1-3-3

-2-1-3-2

+1-1-3-3

0+1-3-2

+1-1-3-3

0+1-3-2

-1-3-3-3

+2-1-5-2

-1-3-3-3

+2-1-3-2

-3-3-3-3

-2-3-3-2

-3-3-3-3

-2-3-3-2

CON

Coarsening G(2) UncoarseningG(2)

-3-1-1-3

-2-3-3-2

-3-1-1-3

-2-3-3-2

-3 -3+1-1

-2-3-1-2

-3-3+1-1

-2-3-1-2

-3-3-1+1

-2-3+10

-3-3-1+1

-2-3+10

-3-3-3-1

-2-3-1+2

-3-3-3-1

-2-3-1+2

-3-3-3-3

-2-3-3-2

Initial

partitioning

Step 0

Fiduccia-Mattheyses

Step 1Step 2Step 3

Step 4

Step 5Step 6

Step 7

Step 8


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15

Solution method

TAClassical graph partitioning algorithms

Adaptive multi-start Multi-level evolutionary biasing (EB)

dge-cut

FOR

Greed Gra h Growin Partitionin Clustered Ada tive Multi-Start Random Greed Gra h Growin Partitionin

E

Distance from global optimum

MODSOL

ut

600000

700000

t

Minimum value

Optimal value

ut

ANA600000

700000

t

Minimum value

Optimal value

Edge-c

Floyd-Warshal300000

400000

500000

E

dge-cu

Edge-c

CON300000

400000

500000

E

dge-cu

Nbr. of attempts

Adaptive Multi-StartNaive Multi-StartSingle attempt

ree y rap row ng ar on ng 1 10000 500 1000

Nbr. of attempts

1 10000 500 1000


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16

Solution method

TAGraph resolution

BSC vs BTS

BSC-level

FOR

Sorted Heavy

Edge Matching

MODSOL

Site

Site-level

graph

ANA

Matching

Cell-level

graph CON


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Performance analysis

TA1 The tracking area re-planning problem


3 Solution method FOR


Analysis set-up

SOL

na ys s resu s

5 ConclusionsANA

CON


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18


TAAnalysis set-up

Goal Check time correlation of graphs in a real GERAN network

Estimate benefit of different LA re-plan approaches in a real network

Check number of changes and population ratio affected by LA changes

Scenario 1 NMS (5498 BTSs, 54 BSCs, 50 LAs)

FOR

Methodology 0) Read NMS data of 4 weeks 2 weeks + 2 weeks one month later

1) Build BSC-level graphs HO [ij], paging/CS/LU [ ]

SOL( ) ( ) ( )

,,s pk c

i i i 2) Compute graph correlation

3) Define periods of high correlation k-means clustering

4 Com ute LA lans ML evolutionar biasin B =400000ANA

( , )u v

Initial operator solution (k=50) Overall, daily, periodic (perfect estimation, imperfect estimation,

imperfect estimation with local optimisation) CON

Criteria Total edge cut ( Overall network signalling cost)

Total number/weight of changes ( Nbr. of BSCs/users changing LA)( )ci


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19


TAAnalysis set-up

Network area

FOR

SOL

ANA

CON

Cell-level graph BSC-level graph


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20


TAAnalysis results

Graph correlation: BTS level

Vertex weight Edge weight

25

1

FOR

25

1

20

0.9

0.95

SOL20

0.9

0.95

10

15

0.85

ANA10

15

0.85

5

0.75

.

CON

5

0.8

| |

1 ( ) ( )

( ) ( ) ( ) ( )1( , )

| |

E

s s s s

s u v

u u v vu v

E

=

=

| |

1 ( ) ( )

( ) ( ) ( ) ( )1( , )

| |

V

s s s s

s u v

u u v vu v

V

=

=


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21


TAAnalysis results

Graph correlation: BSC level

Vertex weight Edge weight FOR

25

0.95

1

25

0.995

1

SOL20

0.85

0.920

0.98

0.985

0.99

ANA10

15

0.8

10

15

0.965

0.97

0.975

CON

5

0.7

.

5

0.95

0.955

0.96

| |

1 ( ) ( )

( ) ( ) ( ) ( )1( , )

| |

E

s s s s

s u v

u u v vu v

E

=

=

| |

1 ( ) ( )

( ) ( ) ( ) ( )1( , )

| |

V

s s s s

s u v

u u v vu v

V

=

=


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22


TAna ys s resu s

Automatic clustering of measurement periods

KK

9 K-means FOR1

arg min ( , )ss

sC C

d=

1

arg min (1 ( , ))ss

sC C

=

SOL

=

K=3 K=4

ANAK=5 K=6

CONK=7 K=8

5 10 15 20 25 5 10 15 20 25

Separate plan for business days and weekends


23/32

23


TAAnalysis results

Comparison of methods operator vs overall optimised solution

FOR

4,00

5,00

opsolution

overall

SOL

2,00

3,00

nalling

cost

ANA0,00

1,00

Sig

CON

Sun

Mo

n

Tue

We

d

Thu

FriSatSun

Mo

n

Tue

We

d

Thu

FriSatSun

Mo

n

Tue

We

d

Thu

FriSatSun

Mo

n

Tue

We

d

Thu

FriSatSun

Time

gna ng cost more t an a ve y merg ng s


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24


TAAnalysis results

Comparison of methods overall vs daily optimised solution1,80

FOR

1,50

1,60

1,70

ng

cost

daily

SOL1,30

1,40

Signalli

ANA

,Sun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

40

50

overall

daily

[BSCs]

CON

20

30

br.ofchan

ges

Too many changesin the network

0

10

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Mon

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Time


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25


TAAnalysis results

Comparison of methods overall vs daily optimised solution1,80

FOR

1,50

1,60

1,70

ng

cost

daily

SOL1,30

1,40

Signalli

ANA0,8

1

o

overall

daily

,Sun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

CON

0,4

0,6

Populatio

nrati

Too many usersaffected by

chan es fre uentl

0

,

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Mon

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Time


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26


TAAnalysis results

Comparison of methods daily vs period1,80

1,50

1,60

1,70

ngc

ost

daily

period

FOR

1 20

1,30

1,40

Signalli

SOL

er o - ase me oachieves near-optimal

performance

,Sun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

ANA40

50

overall

daily

[BSCs]

CON

20

30

Nbr.ofchanges

with less changesin the network

0Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Mon

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Time


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TAAnalysis results

Comparison of methods perfect vs imperfect estimation1,80

1,50

1,60

1,70

ing

cost

daily

period

period est

FOR

1,20

1,30

1,40

Signalli

SOLmight lead to

forbidden solutions

1 week is not enou hSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

40

50

overall

daily ANA

for predicting

[BSCs]

20

30

Nbr.ofcha

nges

period est

CON

0Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Mon

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Time


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TAAnalysis results

Comparison of methods perfect vs imperfect estimation with overload factor1,80 'k k

FOR

1,50

1,60

1,70

ng

cost

dailyperiod

period est (r=1.05)

i ir=

SOL

1,20

1,30

1,40

Signalli

Building estimates fromseveral week is better

than using overload factor

40

50

overall

daily ANA

Sun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

[BSCs]

20

30

br.ofchanges

period est (r=1.05)

CON

0Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Mon

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Time


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TAAnalysis results

Comparison of methods local optimisation process1,80

FOR

1,50

1,60

1,70

gcost

overall

daily

period

period est (r=1.05)

SOL1,30

1,40

Signalli

period est opt (r=1.05)

Some benefit fromdis lacin chan es

ANA

,Sun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

Mon

Tue

Wed

Thu

FriSatSun

40

50

overall

dailyBSCs]

CON

20

30

br.ofchan

ges period est (r=1.05)

period est opt (r=1.05)

[

0

10

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Mon

Mon

Tue

Wed

Thu

FriSat

Sun

Mon

Tue

Wed

Thu

FriSat

Sun

Time

frequency of changes.


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TAAnalysis results

Comparison of methods

1,7overall

daily

eriod

1,7overall

daily

period

FOR

1,6nallingcost

period est

period est (r=1.05)

period est opt

1,6gn

allingcost

period est

period est (r=1.05)

period est optSOL

Avg.si

Avg.si

ANA

1,50 0,1 0,2 0,3

Avg. populat ion ratio affec ted by changes

1,50 5 10 15

Avg. nbr. of changesCON

[BSCs]

Period-based TA optimisation has the best trade-offbetween signalling cost and number of changes


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Conclusions

TA



3 Solution methodFOR

SOL

Main results

Open issues ANA

CON


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Conclusions

TAMain results

Problem formulation

9 Possible to use commercial partitioning packages for TA planning problem

Graph correlation

FOR

e wor grap s s ow g corre a on e ween us ness ays or wee -en s

9 Correlation becomes smaller as time goes by

9 Graph correlation coefficient can be used to detect need for re-planning

SOL

Solution method

9 Most of the benefit of TA re-planning is obtained by changing plan twice a week ANA

9 Need for averaging measurements over several weeks to build reliable graphs

Open issues CON

New TA concepts TA list, overlapping TAs

Dynamic approaches Reactive (e.g., problem-triggered) method

SOCRATES Final Workshop Matias Toril

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