Capacity Optimization for Self-organizing Networks: Analysis and Algorithms

Philipp Hasselbach

Capacity Optimization for Self-organizing Networks: Analysis and AlgorithmsPhilipp Hasselbach

2Philipp Hasselbach

Inhomogeneous capacity demand Rush hour traffic Concerts, sports tournaments Change in user behaviour

Motivation

Capacity Optimization As much capacity as

required At all times and all places Achieved by allocation of

cell bandwidth and transmit power to the cells

1tt

2tt

3Philipp Hasselbach

Capacity in Cellular Networks

Downlink considered

Link capacity influencing factors User position Attenuation Shadowing Inter-cell interference

Cell capacity influencing factors User distribution Service type Scheduling

Transmit powerCell bandwidthInter-cell inter-ference powerSINR of user kNoise power

txP

NPk

IPB

N1 ,P

BP ,txIP

N2 ,P

IP

4Philipp Hasselbach

Self-organizing Networks (SONs)

Drivers High complexity of mobile radio

technology Operation of several networks of different

technologies Need to reduce CAPEX and OPEX

Autonomous operation In configuration, optimization, healing Circumventing classical planning and

optimization processes

SONS: Shift of paradigm Source:FP7 SOCRATES

5Philipp Hasselbach

Automatic Capacity Optimization for SONs

Real-time capabilities Treatment of large networks Accurate results Reliable operation

Depends onuser distributionenvironmentInter-cell interference (ICI)

Interdependencies among cells and users

Capacity optimizationSON requirements

Source:FP7 SOCRATES

High complexity, excessive signaling

6Philipp Hasselbach

Outline

Cell-centric Network Model Requirements and Derivation PBR- and PBN-Characteristic

Automatic Capacity Optimization for SONs Self-Organizing Approach Network State evaluation Network Capacity Optimization

Simulation and Analysis Functional Analysis Real-World Analysis

Summary

7Philipp Hasselbach

Outline




Summary

8Philipp Hasselbach

Cell-centric Network Model: Requirements

Application for allocation of resources cell bandwidth and transmit powers to the cells Modeling of the relation between cell bandwidth, transmit power and cell

performance Low complexity

Consideration of User QoS requirements User distribution Environment Inter-cell interference Interdependencies among cells

9Philipp Hasselbach

Cell-centric Network Model

User bit rate Cell throughput PBR-Characteristic

•SINR measurements•User distribution, environment model

10Philipp Hasselbach


User bit rate

•Number of users•User QoSrequirements

Cell throughput PBR-Characteristic

•User bit rate pdf•empiric• theoretic




Ce

ll t

hro

ug

hp

ut

in M

bit

/s

User bit rate


Cell throughput PBR-Characteristic

•Outage probability p•Cell bandwidth B•Transmit power P


•Cell throughput cdf•empiric• theoretic


p


PBR- and PBN-Characteristic

PBR-Characteristic Relates transmit power, cell bandwidth,

cell throughput of cell i

PBN-Characteristic Relates transmit power, cell bandwidth,

number of users of cell i

Cel

l th

rou

gh

pu

t in

Mb

it/s

Nu

mb

er o

f u

sers

iii BfR ,R

iii BfN ,N







Cel

l th

rou

gh

pu

t in

Mb

it/s

Nu

mb

er o

f u

sers

iii BfR ,R

iii BfN ,N

Power ratio: relates transmit powerto average inter-cell interference power







Cel

l th

rou

gh

pu

t in

Mb

it/s

Nu

mb

er o

f u

sers

iii BfR ,R

iii BfN ,N

Available for different schedulers

Available for different schedulers


Outline




Summary


Self-organizing Approach

Self-organizing control loop: Network state optimization Application of PBR-/PBN-Characteristic Determination of possible performance Comparison with required performance Decision to take action

Network capacity optimization Definition of optimization problems Application of PBR-/PBN-Characteristic

in objective function and constraints Solution of optimization problems to

obtain resource allocation to cells Constant cell sizesC

ell

thro

ug

hp

ut

in M

bit

/s

Collectionof measure-ments

Networkstateevaluation

Networkcapacityoptimisation

Cellularradio

network


Network State Evaluation

Current network state Number of users in cell i: Cell bandwidth: Power ratio:

Number of users that can be supported by the cell (obtained from PBN-Characteristic):

: no action

: network optimization

iN~

iB

Nu

mb

er o

f u

sers

i

iBi

Nii NN ~

Nii NN ~

iN

iii BfN ,N


Network Capacity Optimization

yfeasibilit

bandwidth cellmax.

powertransmit max.

QoSusermin.s.t.

,Capacitymax,

BPBP

Network through-put R

Total number of users N

Transmit power P

Cell band-width B

Joint P,B

i

iii BRR ,

i

iii BNN ,

Optimization problems Optimization approaches


Network Capacity Optimization

yfeasibilit

bandwidth cellmax.

powertransmit max.

QoSusermin.s.t.

,Capacitymax,

BPBP

Network through-put R

Total number of users N

Transmit power P

Cell band-width B

Joint P,B

i

iii BRR ,

i

iii BNN ,

Optimization problems Optimization approaches

Central and distributed solving algorithms for analysis and implementation


Outline




Summary


Simulation Approach for Functional Analysis

Inhomogeneous capacity demand: hotspot scenarios users in hotspot cell, users in

non-hotspot cell

Hotspot factor

Wrap-around technique to avoid border effects

Evaluation of capacity optimization approaches w.r.t. hotspot distribution

Evaluation for different hotspot strengths

w/o coordination of bandwidth allocations of neighbored cells Mitigation of inter-cell interference

LTE-typical simulation parameters

hsN 0N

Single hotspot scenario Cluster hotspot scenario Multi hotspot scenario

0

hshs N

N


Simulation Parameters for Functional Analysis

Cell radius R 250 m

Number of cells 39

User distribution uniform

Propagation model 3GPP SCM Urban Macro

Shadow fading variance 8 dB

Max. transmit power 33 dBm

Total system bandwidth 10 MHz

Scheduling PF, FT

Data rate per user 100 kbit/s


Network Throughput Optimization, Single Hotspot Scenario

PF scheduling FT scheduling


Network Throughput Optimization, Coordinated Bandwidth Allocations

Cluster HS Scenario Multi HS Scenario


Functional Analysis: Summary

Adaptation of the network to inhomogeneous capacity demands achieved For strong inhomogeneous capacity demand coordination of bandwidth

allocations required For FT scheduling coordination of bandwidth allocations required

Transmit power allocation favorable with clustered hotspot cells Cell bandwidth allocation and joint allocation favorable with distributed

hotspot cells


Simulation Approach for Real-World Analysis

Scenario based on real network Network footprint from existing network Downtown area, 50 km², 46 sites, 126 sectors Pilot power receive strength prediction for each sector

Determination of cell borders

Inhomogeneous capacity demand According to user distribution estimation Based on DL throughput measurements 229 snapshots over 5 days

Performance analysis Consideration of snapshots 10-50 Evaluation of performance in strongest hotspots


Real-World-Analysis: Hotspot Strength and Strongest Hotspots

Strongest hotspotMaximum hotspot strength



Strongest hotspotMaximum hotspot strength



Strongest hotspotNetwork throughput,FT scheduling


Outline




Summary


Summary

Cell-centric network modeling proposed PBR- and PBN-Characteristic Provides accurate modeling for automatic capacity optimization for SONs Avoids high complexity and high signaling effort

Self-Organizing Approach proposed Application of cell-centric network model Central and distributed implementations for analysis and practical

implementation

Simulative verification In artificial scenarios and real-world scenario Adaptation of the network to inhomogeneous capacity demands shown


Backup


kk

k

RB

1log

~

2

bit,

Power-Bandwidth Characteristics

f

Itx~ ,,~

PPBf kBk

,, rfrUser distribution

PDF of the bandwidth

required by user k

Bandwidth required

by user k

,, rfr

K

kkBB

1cell

~~ Bandwidth required

by the whole cell

PDF of the bandwidth

required by the cell

),(~),,~

( 2cellcellItxcell~

cellΝPPBf

B

K independent users

Central Limit Theorem

),,( Itxcell PPBF


Cell Outage Probability

CDF of the bandwidth required by the cell Probability that sufficient bandwidth is

allocated

Cell outage probability Probability that allocated bandwidth is

not sufficient

),,(1 Itxcellcell PPBFp

cell1 p

),,(~

Prob Itxcellcellcell PPBFBB

Bandwidth required by the cellcell

~B

cellB


Fluctuating capacity demand Rush hour traffic Concerts, sports tournaments Change in user behaviour Change in environment

Motivation

Capacity Optimization As much capacity as

required At all times and all places

1tt

2tt



Real-time capabilities Accurate results Reliable operation

Complex modeling Large number of users and BSs Effects of the user distribution Effects of the environment Interdependencies among cells and

users

Source:FP7 SOCRATES

Capacity optimizationSONs



Real-time capabilities Accurate results Reliable operation

Complex modeling Effects of the user distribution Effects of the environment Inter-cell interference (ICI) Interdependencies among cells and

users

Capacity optimizationSONs

Source:FP7 SOCRATES



•User distribution, environment model

•SINR measurements

•Outage probability•Cell bandwidth•Transmit power

User QoS requirements

Ce

ll th

rou

gh

pu

t in

Mb

it/s



•Outage probability•Cell bandwidth B•Transmit power P





Cell Performance for (B,P)


PBR-Characteristic

Reduced complexity due to focus on cells

User QoS requirements considered Relation between cell bandwidth,

transmit power and cell performance

Ce

ll th

rou

gh

pu

t in

Mb

it/s

Cell Performance for (B,P)

•For different•Cell bandwidth B•Transmit power P


Model the interdependence oftransmit power and cellbandwidth

Contain information on userdistribution, environment,inter-cell interference

Analytic derivation available Measurement based derivation

available, determined fromstandard system measurements(attenuation, SINR)


Modeling equations

RandomVariable

transformation

Measurementdata

transformation

SIN

R m

easu

rem

entsU

ser distribution

Environm

ent model

Theoretic Approach Practical Approach





Number ofusers

•Outage definition•Cell bandwidth•Transmit power


Automatic Capacity Optimization Approaches

I take SC 1. OK, I

take SC 2

Can I take SC 1?

Uncoordinated/scheduling based (State of the art):

Coordinated (new):

+ : easy implementation- : Collisions, QoS?

+ : Collisions can be avoided QoS- : Complexity? Implementation?

I take SC 1.

LocalScheduling Local

Scheduling

Inter-BScommunication

SC1 SC1

SC1SC2


Two Alternative SO Approaches

I take SC 1. OK, I

take SC 2

Can I take SC 1?

Uncoordinated: Coordinated:

I take SC 1.

LocalScheduling Local

Scheduling

Inter-BScommunication

SC1 SC1

SC1SC1

Power-Bandwidth Characteristicfor performance analysis

Power-Bandwidth Characteristicfor approach realization and per-formance analysis


General System Concept

Hierarchical approach

Resource allocation to users,no inter-cell scheduling

Sched.cell 1

Sched.cell 2

Sched.cell N

Resource allocation to cells

Source: 3GPP

Networkstateevaluation

Networkparameteroptimisation

Networkparameteradjustment

Self-organising functionality/Self-organising control loop

Capacity Optimization for Self-organizing Networks: Analysis and Algorithms

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