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Capacity Optimization for Self-organizing Networks: Analysis and Algorithms. Philipp Hasselbach. Motivation. Inhomogeneous capacity demand Rush hour traffic Concerts, sports tournaments Change in user behaviour. Capacity Optimization As much capacity as required

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Capacity optimization for self organizing networks analysis and algorithms

Capacity Optimization for Self-organizing Networks: Analysis and Algorithms

Philipp Hasselbach

Philipp Hasselbach


Motivation
Motivation and Algorithms

  • Inhomogeneous capacity demand

    • Rush hour traffic

    • Concerts, sports tournaments

    • Change in user behaviour

  • Capacity Optimization

    • As much capacity asrequired

    • At all times and all places

    • Achieved by allocation of cell bandwidth and transmit power to the cells

Philipp Hasselbach


Capacity in cellular networks

Downlink considered and Algorithms

Link capacity influencing factors

User position

Attenuation

Shadowing

Inter-cell interference

Cell capacity influencing factors

User distribution

Service type

Scheduling

Transmit power

Cell bandwidth

Inter-cell inter-ference power

SINR of user k

Noise power

Capacity in Cellular Networks

Philipp Hasselbach


Self organizing networks sons

Drivers and Algorithms

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

Source:FP7 SOCRATES

Self-organizing Networks (SONs)

SONS: Shift of paradigm

Philipp Hasselbach


Automatic capacity optimization for sons

Real-time capabilities and Algorithms

Treatment of large networks

Accurate results

Reliable operation

Depends on

user distribution

environment

Inter-cell interference (ICI)

Interdependencies among cells and users

Source:FP7 SOCRATES

Automatic Capacity Optimization for SONs

SON requirements

Capacity optimization

High complexity, excessive signaling

Philipp Hasselbach


Outline
Outline and Algorithms

  • 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

Philipp Hasselbach


Outline1
Outline and Algorithms

  • 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

Philipp Hasselbach


Cell centric network model requirements
Cell-centric Network Model: Requirements and Algorithms

  • 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

Philipp Hasselbach


Cell centric network model
Cell-centric Network Model and Algorithms

User bit rate

PBR-Characteristic

Cell throughput

  • SINR measurements

  • User distribution, environment model

Philipp Hasselbach


Cell centric network model1
Cell-centric Network Model and Algorithms

User bit rate

PBR-Characteristic

Cell throughput

  • User bit rate pdf

    • empiric

    • theoretic

  • SINR measurements

  • User distribution, environment model

  • Number of users

  • User QoSrequirements

Philipp Hasselbach


Cell centric network model2
Cell-centric Network Model and Algorithms

User bit rate

PBR-Characteristic

Cell throughput

  • Cell throughput cdf

    • empiric

    • theoretic

  • User bit rate pdf

    • empiric

    • theoretic

  • SINR measurements

  • User distribution, environment model

  • Number of users

  • User QoSrequirements

  • Outage probability p

  • Cell bandwidth B

  • Transmit power P

Cell throughput in Mbit/s

p

Philipp Hasselbach


Pbr and pbn characteristic

PBR-Characteristic and Algorithms

Relates transmit power, cell bandwidth, cell throughput of cell i

PBN-Characteristic

Relates transmit power, cell bandwidth, number of users of cell i

PBR- and PBN-Characteristic

Cell throughput in Mbit/s

Number of users

Philipp Hasselbach


Pbr and pbn characteristic1

PBR-Characteristic and Algorithms

Relates transmit power, cell bandwidth, cell throughput of cell i

PBN-Characteristic

Relates transmit power, cell bandwidth, number of users of cell i

PBR- and PBN-Characteristic

Cell throughput in Mbit/s

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

Number of users

Philipp Hasselbach


Pbr and pbn characteristic2

PBR-Characteristic and Algorithms

Relates transmit power, cell bandwidth, cell throughput of cell i

PBN-Characteristic

Relates transmit power, cell bandwidth, number of users of cell i

PBR- and PBN-Characteristic

Cell throughput in Mbit/s

Available for different schedulers

Number of users

Available for different schedulers

Philipp Hasselbach


Outline2
Outline and Algorithms

  • 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

Philipp Hasselbach


Self organizing approach

Self-organizing control loop: and Algorithms

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 sizes

Networkstateevaluation

Collection

of measure-ments

Networkcapacityoptimisation

Cell throughput in Mbit/s

Cellularradionetwork

Self-organizing Approach

Philipp Hasselbach


Network state evaluation

Current network state and Algorithms

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

Number of users

Network State Evaluation

Philipp Hasselbach


Network capacity optimization
Network Capacity Optimization and Algorithms

Optimization problems

Optimization approaches

Philipp Hasselbach


Network capacity optimization1
Network Capacity Optimization and Algorithms

Optimization problems

Optimization approaches

Central and distributed solving algorithms for analysis and implementation

Philipp Hasselbach


Outline3
Outline and Algorithms

  • 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

Philipp Hasselbach


Simulation approach for functional analysis

Inhomogeneous capacity demand: hotspot scenarios and Algorithms

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

Simulation Approach for Functional Analysis

Single hotspot scenario

Multi hotspot scenario

Cluster hotspot scenario

Philipp Hasselbach


Simulation parameters for functional analysis
Simulation Parameters for Functional Analysis and Algorithms

Philipp Hasselbach


Network throughput optimization single hotspot scenario
Network Throughput Optimization, Single Hotspot Scenario and Algorithms

PF scheduling

FT scheduling

Philipp Hasselbach


Network throughput optimization coordinated bandwidth allocations
Network Throughput Optimization, Coordinated Bandwidth Allocations

Cluster HS Scenario

Multi HS Scenario

Philipp Hasselbach


Functional analysis summary
Functional Analysis: Summary Allocations

  • 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

Philipp Hasselbach


Simulation approach for real world analysis
Simulation Approach for Real-World Analysis Allocations

  • 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

Philipp Hasselbach


Real world analysis hotspot strength and strongest hotspots
Real-World-Analysis: Hotspot Strength and Strongest Hotspots Allocations

Maximum hotspot strength

Strongest hotspot

Philipp Hasselbach


Real world analysis hotspot strength and strongest hotspots1
Real-World-Analysis: Hotspot Strength and Strongest Hotspots Allocations

Maximum hotspot strength

Strongest hotspot

Philipp Hasselbach


Real world analysis hotspot strength and strongest hotspots2
Real-World-Analysis: Hotspot Strength and Strongest Hotspots Allocations

Network throughput,FT scheduling

Strongest hotspot

Philipp Hasselbach


Outline4
Outline Allocations

  • 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

Philipp Hasselbach


Summary
Summary Allocations

  • 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

Philipp Hasselbach


Backup
Backup Allocations

Philipp Hasselbach


Power bandwidth characteristics
Power-Bandwidth Characteristics Allocations

User distribution

PDF of the bandwidth

required by user k

K independent users

Central Limit Theorem

Bandwidth required

by user k

PDF of the bandwidth

required by the cell

Bandwidth required

by the whole cell

Philipp Hasselbach


Cell outage probability

CDF of the bandwidth required by the cell Allocations

Probability that sufficient bandwidth is allocated

Cell outage probability

Probability that allocated bandwidth is not sufficient

Cell Outage Probability

Bandwidth required by the cell

Philipp Hasselbach



Motivation1
Motivation Allocations

  • Fluctuating capacity demand

    • Rush hour traffic

    • Concerts, sports tournaments

    • Change in user behaviour

    • Change in environment

  • Capacity Optimization

    • As much capacity asrequired

    • At all times and all places

Philipp Hasselbach


Automatic capacity optimization for sons1

Real-time capabilities Allocations

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

Automatic Capacity Optimization for SONs

SONs

Capacity optimization

Philipp Hasselbach


Automatic capacity optimization for sons2

Real-time capabilities Allocations

Accurate results

Reliable operation

Complex modeling

Effects of the user distribution

Effects of the environment

Inter-cell interference (ICI)

Interdependencies among cells and users

Source:FP7 SOCRATES

Automatic Capacity Optimization for SONs

SONs

Capacity optimization

Philipp Hasselbach



Cell centric network model3
Cell-centric Network Model Allocations

  • User distribution, environment model

  • SINR measurements

User QoS requirements

  • Outage probability

  • Cell bandwidth

  • Transmit power

Cell throughput in Mbit/s

Philipp Hasselbach


Cell centric network model4
Cell-centric Network Model Allocations

  • User bit rate pdf

    • empiric

    • theoretic

  • Number of users

  • User QoSrequirements

  • User distribution, environment model

  • SINR measurements

  • Outage probability

  • Cell bandwidth B

  • Transmit power P

Cell Performance for (B,P)

Philipp Hasselbach


Pbr characteristic

Reduced complexity due to focus on cells Allocations

User QoS requirements considered

Relation between cell bandwidth, transmit power and cell performance

PBR-Characteristic

Cell Performance for (B,P)

  • For different

    • Cell bandwidth B

    • Transmit power P

Cell throughput in Mbit/s

Philipp Hasselbach



Cell centric network model5

Model the interdependence of Allocationstransmit power and cellbandwidth

Contain information on userdistribution, environment,inter-cell interference

Analytic derivation available

Measurement based derivation available, determined fromstandard system measurements(attenuation, SINR)

User distribution

Environment model

SINR measurements

Random

Variable

transformation

Measurement

data

transformation

Modeling equations

Cell-centric Network Model

Theoretic Approach

Practical Approach

Philipp Hasselbach


Cell centric network model6
Cell-centric Network Model Allocations

  • User distribution, environment model

  • SINR measurements

  • Outage definition

  • Cell bandwidth

  • Transmit power

Number ofusers

Philipp Hasselbach



Automatic capacity optimization approaches
Automatic Capacity Optimization Approaches Allocations

Uncoordinated/scheduling based (State of the art):

Coordinated (new):

Can I take SC 1?

I take SC 1.

OK, I take SC 2

I take SC 1.

SC1

SC2

SC1

SC1

Inter-BScommunication

LocalScheduling

LocalScheduling

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

+ : easy implementation- : Collisions, QoS?

Philipp Hasselbach


Two alternative so approaches
Two Alternative SO Approaches Allocations

Uncoordinated:

Coordinated:

Can I take SC 1?

I take SC 1.

OK, I take SC 2

I take SC 1.

SC1

SC1

SC1

SC1

Inter-BScommunication

LocalScheduling

LocalScheduling

Power-Bandwidth Characteristicfor approach realization and per-formance analysis

Power-Bandwidth Characteristicfor performance analysis

Philipp Hasselbach


General system concept

Network Allocationsparameteroptimisation

Resource allocation to cells

Networkstateevaluation

Networkparameteradjustment

Sched.cell 1

Sched.cell 2

Sched.cell N

Source: 3GPP

General System Concept

Self-organising functionality/

Self-organising control loop

Hierarchical approach

Resource allocation to users,no inter-cell scheduling

Philipp Hasselbach


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