automatic re-planning of tracking areaswebpersonal.uma.es/~toril/files/2011 iwsos ta replanning.pdf1...

1

Automatic Re-planning of Tracking Areas

Matías TorilCommunications Engineering Dept., University of Málaga, 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

Outline

1 The tracking area re-planning problem

2 Graph-theoretic formulationp

3 Solution method

4 Performance analysis

5 Conclusions

3

Outline

LAIntroTA

1 The tracking area re-planning problem

Location area planning in legacy networks

LAIntroTA

State of research and technology FOR

2 Graph-theoretic formulation

3 Solution methodSOL


5 C l iANA

5 Conclusions

CON

4

The tracking area planning problem

LAIntroTACellular network structuring

LAIntroTA

PCUPCU

BSCMSC/SGSN

FOR

BSC BSC

LA/RA LA/RA

BSC

PCUPCU

BSC

LA SOL

BSC BSCBSC

PCU PCU PCU PCUPCUPCUPCU

ANA

BTS

Site Site Site Site Site Site

BTS BTSBTSBTS BTSBTSBTSBTSBTSBTS BTSBTSBTS

LA

CON

5

i i ll l k


LAIntroTALocation management in current cellular networks

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

Algorithm Location update and paging (based on location/paging areas)

LAIntroTA

Algorithm Location update and paging (based on location/paging areas)

Problem Trade-off in location area size» Many small LAs ⇒ more LUs (i.e., DCCH capacity, load in databases)» Few large LAs ⇒ more paging requests (i e PCH capacity)

FOR

» Few large LAs ⇒ more paging requests (i.e., PCH capacity)

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

LU req.LA #1 LA #2 ANA

BSC MSC[DCCH] BTS

LU req.CN

PG req.[PCH]

Definition of LAsPaging algorithm

CON

VLR HLR

TMSI/LAC+CIBSSMS

[ ]

LA border

6


LAIntroTAState of research and technology

Current practice

LA plan with BSC (instead of BTS) resolution

LAIntroTA

LA plan with BSC (instead of BTS) resolution

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

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

C t i t th t BSC i th LA b l t th MSC

SOL

MSC/SGSN

• Constraint that BSCs in the same LA belong to the same MSC

• Changes in LA plan lead to temporary congestion of DCCH in affected cellsANA

BSC BSCBSC

LA/RA LA/RA

MSC/SGSNBSCBSC

CON

BTS

Site Site Site Site Site Site

PCU PCU PCU PCU

BTS BTSBTSBTS BTSBTSBTSBTSBTSBTS BTSBTSBTS

7


St t f h d t h l LAIntroTAState of research and technology

New drivers

Changes in vendor equipment LA borders can now cross MSC borders

LAIntroTA

Changes in vendor equipment LA borders can now cross MSC borders

New network algorithms Overlapping TAs [3GPP rel. 7], tracking area list [3GPP rel. 8]Interest on SON NGMN [SON use cases, O&M requirements], 3GPP [Rel. 8/9 LTE]

R l t d k

FOR

Related work

Graph partitioning Local refinement [Plehn 95], integer programming [Tcha 97],genetic algorithm [Gondim 96], simulated annealing [Demirkol 04],linear programming [Bejerano 06] set covering [Lo 04]

SOL

linear programming [Bejerano 06], set covering [Lo 04]

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

Dynamic adaptation Trade-off signalling versus reconfiguration cost [Modarres 09]

Adjustment of TA overlapping [Varsamopoulos 04]ANA

Adjustment of TA overlapping [Varsamopoulos 04]

Main contributions

Re-formulation of TA planning as a classical graph partitioning problem

h d f l b d h kCON

Method to optimise TAs frequently based on statistics in the network management

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

8

Graph-theoretic formulation

TA1 The tracking area re-planning problem in GERAN


TA

Naïve formulation

Adapted advanced formulation

ALGFOR

3 Proposed methodSOL


5 ConclusionsANA

5 Conclusions

CON

9


TANaïve formulation

PCU 1 PCU 2 LA 1 LA 2

TA

Cell 1

1ω

5ω 2ω12γ15γ

ALGFOR

Cell 2

Cell 5

14γ

3ω45γ

4ω23γNetwork

model

SOL

Cell 3

Cell 4

34γmodel

( )

• Network area optimised:

Traditionally 1 MSC/VLRANA

(TAP) Minimise

subject toOptimisationd l

Traditionally 1 MSC/VLR

Currently 1 NMS

CONmodel

10


TAAdapted advanced formulation

TA

1) Paging cost in objective function

2) Paging cost term π paging constraint term ALGFOR

3) Time dependence

LU-to-HO ratio

Paging-to-LU cost ratio

'γ

SOL

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

( )k k

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

r cδ δ

γ ω ω ω∈ ∉

⎛ ⎞⎜ ⎟⎜ ⎟⎝ ⎠

+ + +∑ ∑ ∑( )( , )

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

r S S ccγ ω ω ω− + ++ ∑∑ ( )( )( , )( , )

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

r Sc cγ ω ω ω ω ω⎛ ⎞

− + − + +⎜ ⎟⎝ ⎠

+ ∑ ∑∑ijγ

( )( )( , )

( ) 1 ij i j iji j E

r Scγ ω ω∈

− + −∑ ( )( )( ) ( ) ( )

( , )( ) 1 s s s

ij i j iji j E

r Scγ ω ω∈

− + −∑ ( )( )( ) ( ) ( )

( , )[ ] [ ] [ ] [ ] [ ]( ) 1

ij

s s si j ij

t i j Et t t t tr Scγ ω ω

∈− + −∑ ∑ ANA

subject to( )

n

pki aw

i VBω

∈<∑ ( )[ ]

n

pki aw

i Vt Bω

∈<∑

CON

11

The assignment of PCUs in GERAN

TA1 The location area re-planning problem in GERAN


TA

3 Solution method

Proposed methodology

FOR


Classical graph partitioning algorithms

Graph resolutionMODSOL

4 Performance analysis ANA

5 Conclusions

CON

12

Solution method

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

2) Minimise impact of changing the TA plan

3) Minimise network signalling cost when re planning TAs

TA

3) Minimise network signalling cost when re-planning TAs


FOR

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

2) Collect network stats in several periods ⇒ HO, LU, CS traffic, total/peak pagingSOL

( ) ( ) ( )k3) Build network graphs ⇒

4) Compute graph correlation between periods ⇒

) d f l d d Cl l h ( k )

ANA

( ) ( ) ( ), ,ij

s s pki iγ ω ω

( , )u vρ

5) Identify correlated measurement periods ⇒ Clustering algorithm (e.g., k-means)

6) Compute TA plan for correlated periods ⇒ Classical graph partitioning algorithmin 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)( )ciω

13

Solution method

TAGraph correlation

Definitions

TA

G(2)

FORG(1)

[ ] ( , )

[ ] [ ] ( )

ij

i

i j E

i V

γ γ

ω ω

= ∀ ∈

= ∀ ∈Ω

SOL

G(0)

| |

[ ] ( , ) ,

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

s s s s

i j E i V

u u v vu v

γ ω

γ γ γ γρ

Ω = ∀ ∈ ∀ ∈

⎛ ⎞⎛ ⎞− −= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟∑

30

0.99

1

ANA

1 ( ) ( )

| |

( , )| |

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

s u v

Vs s s s

u vE

u u v vu vV

γγ γ

ω

ρσ σ

ω γ ω ωρσ σ

=

= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠⎛ ⎞⎛ ⎞− −

= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠

∑

∑20

25

0.97

0.98

CON

1 ( ) ( )| |

1( , )

s u vV

u v

ω ωσ σ

ρ

=

Ω

⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠

=| | | |

1 ( ) ( )| | | |

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

s u vE V

u u v vσ σ

+

= Ω Ω+

⎛ ⎞⎛ ⎞Ω −Ω Ω −Ω⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠

∑5

10

15

0.94

0.95

0.96

Graph correlation coefficientand clusters

5 10 15 20 25 30 0.93

14

Solution method

TAClassical graph partitioning algorithms

Refinement algorithms

G d l ith (GR)

Multi-level refinement

C i l ith

TA

Greedy algorithm (GR)

Kernighan-Lin algorithm (KL)

Fiduccia-Mattheyses algorithm (FM)

Coarsening algorithm

Initial partitioning

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)

G(m)

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 +1 0

-3 -3 -1 +1

-2 -3 +1 0

-3 -3 -3 -1

-2 -3 -1 +2

-3 -3 -3 -1

-2 -3 -1 +2

-3 -3 -3 -3

-2 -3 -3 -2

G(m)

Initialpartitioning

Step 0

Fiduccia-Mattheyses

Step 1Step 2Step 3

Step 4

Step 5Step 6

Step 7

Step 8

15

Solution method

TAClassical graph partitioning algorithms

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

TA

Edg

e-cu

t

FOR

Greedy Graph Growing Partitioning Clustered Adaptive Multi-StartRandom Greedy Graph Growing Partitioning

E

Distance from global optimumMODSOL

y p g g

ut

p y p g g

600000

700000

t

Minimum valueOptimal value

ut ANA600000

700000

t

Minimum valueOptimal value

Edg

e-cu

Floyd-Warshal G d G h G i P titi i 300000

400000

500000

Edg

e-cu

Edg

e-cu

CON300000

400000

500000

Edg

e-cu

t

Nbr. of attempts

Adaptive Multi-StartNaive Multi-StartSingle attempt

Greedy Graph Growing Partitioning 300000

1 10000 500 1000

Nbr. of attempts

300000

1 10000 500 1000

16

Solution method

TAGraph resolution

BSC vs BTS

TA

BSC-level graph

FOR

Sorted Heavy

Edge Matching

graph

MODSOL

Site

Site-level graph

ANA

Matching

Cell-levelgraph CONg p

17

Performance analysis

TA1 The tracking area re-planning problem


TA

3 Solution methodFOR


Analysis set-up

A l i lt

SOL

Analysis results

5 ConclusionsANA

CON

18


TAAnalysis set-up

Goal Check time correlation of graphs in a real GERAN network

TA

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 [γij], paging/CS/LU [ ]SOL

( ) ( ) ( ),,s pk ci i iω ω ω

2) Compute graph correlation ⇒

3) Define periods of high correlation ⇒ k-means clustering

4) Compute LA plans ⇒ ML evolutionary biasing, Baw=400000ANA

( , )u vρ

) Co pute p a s ⇒ e o ut o a y b as g, aw 00000

• 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)( )ciω

19


TAAnalysis set-up

Network area

TA

FOR

SOL

ANA

CON

Cell-level graph BSC-level graph

20


TAAnalysis results

Graph correlation: BTS level

TA

Vertex weight Edge weight

25

1

FOR

25

1

200.9

0.95

SOL20

0.9

0.95

10

15

0 8

0.85

ANA10

15

0.85

5 10 15 20 25

5

0.75

0.8

CON5 10 15 20 25

5

0.8

5 10 15 20 25

| |

1 ( ) ( )

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

Es s s s

s u v

u u v vu vEγ

γ γ

γ γ γ γρσ σ=

⎛ ⎞⎛ ⎞− −= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟

⎝ ⎠⎝ ⎠∑

| |

1 ( ) ( )

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

Vs s s s

s u v

u u v vu vVω

ω ω

ω γ ω ωρσ σ=

⎛ ⎞⎛ ⎞− −= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟

⎝ ⎠⎝ ⎠∑

5 10 15 20 25

21


TAAnalysis results

Graph correlation: BSC level

TA

Vertex weight Edge weight FOR

25

0.95

1

25

0.995

1

SOL20

0.85

0.9 20

0.98

0.985

0.99

ANA10

15

0 75

0.8

0.85

10

15

0.965

0.97

0.975

CON5 10 15 20 25

5

0.7

0.75

5 10 15 20 25

5

0.95

0.955

0.96

5 10 15 20 25

| |

1 ( ) ( )

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

Es s s s

s u v

u u v vu vEγ

γ γ

γ γ γ γρσ σ=

⎛ ⎞⎛ ⎞− −= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟

⎝ ⎠⎝ ⎠∑

| |

1 ( ) ( )

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

Vs s s s

s u v

u u v vu vVω

ω ω

ω γ ω ωρσ σ=

⎛ ⎞⎛ ⎞− −= ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟

⎝ ⎠⎝ ⎠∑

5 10 15 20 25

22

A l i lt


TAAnalysis results

Automatic clustering of measurement periods

TA

2K

∑ ∑ 2K

∑ ∑K-means ⇒ FOR

K 1 K=2

1

2arg min ( , )ss

sC C

d μ= Ω∈

Ω∑ ∑1

2arg min (1 ( , ))ss

sC C

ρ μ= Ω∈

− Ω∑ ∑

SOL

K=1

K=3

K=2

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


TAAnalysis results

Comparison of methods ⇒ operator vs overall optimised solution

TA

FOR

4,00

5,00opsolution

overall

SOL

2,00

3,00

nalli

ng c

ost

ANA0,00

1,00

Sign

CON

, Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

Time

Si lli t th h l d b i LASignalling cost more than halved by merging LAs

24


TAAnalysis results

Comparison of methods ⇒ overall vs daily optimised solution

TA

1,80overall

FOR

1,50

1,60

1,70ng

cos

t

overall

daily

SOL

1 20

1,30

1,40

Sign

allin

ANA

1,20 Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

40

50

overall

daily

[BSC

s]

CON20

30

Nbr

. of c

hang

es

Too many changesin the network

[

0

10

Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atM

onM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

N

Time

25


TAAnalysis results

Comparison of methods ⇒ overall vs daily optimised solution

TA

1,80overall

FOR

1,50

1,60

1,70ng

cos

t

overall

daily

SOL

1 20

1,30

1,40

Sign

allin

ANA0,8

1

o

overall

daily

1,20 Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

CON0 2

0,4

0,6

Popu

latio

n ra

tio

Too many usersaffected by

changes frequently

0

0,2

Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atM

onM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

Time

g q y

26


TAAnalysis results

Comparison of methods ⇒ daily vs period1,80

overall

TA

1,50

1,60

1,70ng

cos

toverall

daily

period

FOR

Period based method

1 20

1,30

1,40

Sign

allin

SOL

Period-based methodachieves near-optimal

performance …

1,20 Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

ANA40

50overall

daily

period[BSC

s]

CON10

20

30

Nbr

. of c

hang

es

period

… with less changesin the network

0

10

Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atM

onM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

N

Time

27


TAAnalysis results

Comparison of methods ⇒ perfect vs imperfect estimation1,80

overall

TA

1,50

1,60

1,70in

g co

stoverall

daily

period

period est

FOR

Estimation errors

1,20

1,30

1,40

Sign

alli

SOL

Estimation errorsmight lead to

forbidden solutions

1 week is not enough, Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

40

50overall

daily

period

ANA

gfor predicting

[BSC

s]

10

20

30

Nbr

. of c

hang

es

period

period est

CON

0

10

Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atM

onM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

Time

28


TAAnalysis results

Comparison of methods ⇒ perfect vs imperfect estimation with overload factor

TA

1,80overall

'( ) ( )pk pkω ωFOR

1,50

1,60

1,70

ng c

ost

overall

daily

period

period est (r=1.05)

( ) ( )pk pki irω ω=

SOL

1,20

1,30

1,40

Sign

alli

Building estimates fromseveral week is better

than using overload factor

40

50overall

daily

period

ANA

1,20 Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

[BSC

s]

10

20

30

Nbr

. of c

hang

es

period

period est (r=1.05)

CON

0

10

Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atM

onM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

N

Time

29


TAAnalysis results

Comparison of methods ⇒ local optimisation process

TA

1,80

FOR

1,50

1,60

1,70

ng c

ost

overall

daily

period

period est (r=1.05)

SOL

1 20

1,30

1,40

,

Sign

allin

period est opt (r=1.05)

Some benefit fromdisplacing changes…

ANA

1,20 Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

40

50overall

daily

displacing changes…

[BSC

s]

CON20

30

Nbr

. of c

hang

es period est (r=1.05)

period est opt (r=1.05)

but increasing the

[

0

10

Sun

Mon

TueW

edThuFriS

atS

unM

onTueW

edThuFriS

atM

onM

onTueW

edThuFriS

atS

unM

onTueW

edThuFriS

atS

un

N

Time

… but increasing thefrequency of changes.

30


TAAnalysis results

Comparison of methods

TA

1,7

overall

daily

period

1,7overall

daily

period

FOR

1,6

gnal

ling

cost

period

period est

period est (r=1.05)

period est opt1,6

gnal

ling

cost period est

period est (r=1.05)

period est optSOL

Avg.

sig

Avg.

sig

ANA

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

Avg. population ratio affected by changes

1,50 5 10 15

Avg. nbr. of changes CON[BSCs]

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

31

Conclusions

TA1 The tracking area re-planning problem


TA

3 Solution method


FOR


5 Conclusions

SOL

5 Conclusions

Main results

Open issues ANA

CON

32

Conclusions

TAMain results

Problem formulation

TA

Possible to use commercial partitioning packages for TA planning problem

Graph correlation

N t k h h hi h l ti b t b i d k d

FOR

Network graphs show high correlation between business days or week-ends

Correlation becomes smaller as time goes by

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

SOL

Solution method

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

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

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