09062009 Simulation of self-x algorithms - FP7 SOCRATES workshop Santander... · Common assumptions...

Slide 1

E3

Simulation of self-x algorithms

Dr.-Ing. Ingo GaspardDeutsche Telekom AG

Slide 2

E3

June 15, 2009 09062009_Simulation_of_self-x_algorithms.ppt

Overview

MotivationSimulation of operator use cases - examples⇒ System level simulation⇒ Cell outage compensation⇒ Home NB parameter optimization⇒ Interference coordination

Knowledge based reconfigurationDynamic spectrum management by RLConclusions

Slide 3

E3


Motivation

• Introduction and deployment of new wireless services and systemsshould be accelerated.

• Usability of future wireless access solutions should be improved (“plug&play”)

• High cost pressure requires improvement of operational efficiency

• Complexity and heterogeneity of radio access networks is dramatically increasing

Self-organization• Self-planning• Self-configuration• Self-optimization • Self-healing • Self-maintenance• …

LTE

UMTS

GSM

WLAN

SON / Self-x functionalities are mandatory for future radio networks!

Slide 4

E3


System level simulation: Common evaluation framework

10 different simulation environments in E3-WP3 categorization of partners’ simulators according to functionalities studiedCommon assumptions and KPI’s for simulation

Class ANetwork & System

Level Simulator

Class BSystem Level

Simulator

Class CSystem Level

Simulator

Categorisation Protocol & Dynamic system behaviour

Dynamic system behaviour, Snap-shot based

Snap-shot/Quasi-static based

Examples of functionalities addressed

JRRM, self-x JRRM, DSM, self-x Self-x

Simulated time scale Up to several hours order of minutes Order of secondsBasic features Modelling of

protocols, dynamic user behaviour

Semi-dynamic change of user behaviour

Quasi-static positions of users

Cell/System configuration Multi-cell, multi-RATs Multi-cell, multi-RATs or single RAT

Multi-cell, single RAT

Main outputs • Network throughput

• Service outage

• System throughput• User TP (DL, UL)• Call/packet

blocking/ dropping rate

• Spectrum utilization

• System throughput

• User distribution

• SIR distribution

Slide 5

E3


⇒ evaluation of self-x algorithms based on system-level simulations for a LTE mobile network.

⇒ Self-x use cases implemented: handover optimization, load balancing, cell outage compensation, and radio parameter optimization for home base stations.

Simulator example: LTE system simulator for self-x

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in k

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Slide 6

E3


Cell outage compensation

Best possible compensation of coverage loss due to BS failure by COC mechanism Trade-off between⇒ Reassignments of lost UEs

to the network ⇒ Additional interference

introduced by compensating cells with changed parameters (e.g. increasedTX Power)

Cell outage situation with a random

set of UEs

Slide 7

E3


Idea of the creation of a compensation networkCompensation network consists of all cells which could participate on COC ⇒ Needs additional information out of daily network operation:

• Neighbour Cell List (NCL) of all cells that appear in a NCL of a cell

• E-UTRAN Cell Identifier (ECI) of all cells appearing in foreign NCLs

• UE IDs of all UEs assigned to foreign cells

Communication via X2 Interface (decentralized solution)⇒ Every cell takes the Neighbour Cell

List (NCL) of all neighbours, detected by its own NCL

⇒ Knowledge of all E-UTRAN CelI Identifier (ECI)s which are useful to compensate cell outage in principal

⇒ Every cell takes constantly the IDs (e.g IMSI) of all UEs that are assigned to their direct neighbours which appear in the NCL

⇒ Knowledge of all UE IDs of affected UEs, if cell outage occurs

⇒ Knowledge of all UEs which are reassigned tocells in the neighbourhood

Cell outage compensation

Simplified example for the information search mechanism

to create a compensation network

Slide 8

E3


Radio parameter optimization of Home Base Stations (HBS) in a co-channel situation⇒ Same RF carrier for HBS and

Macro Base Station (MBS) Trade-off between⇒ Additional resources provided

by each HBS⇒ Additional interference for MBS

and existing HBS introduced by each new HBS

Key benefits⇒ Improved indoor coverage ⇒ Additional capacity provided by

HBS⇒ Cost-efficient connections to the

core network via DSL

Home NB parameter optimization

Co-channel situation for the downlink direction

Useful link DL

Outdoor-Indoor Channel

Indoor Channel Building wall HeNB

Indoor-Outdoor-Indoor Channel

1st floor

2nd floorHeNB

HeNB

HeNB

Interferer DL

Indoor Channel

Indoor Channel

Slide 9

E3


Example for the distributions of mobile stations (MS) and home base stations (HBS)

Each BS – MS link can be characterized by a channel model.Multiple channel models based on WINNER II channel models are implemented.

The following rules are assumed for the selection of the channel model:⇒ If two HBS have a distance less than 20m, both HBS are within the same building (indoor channel). Otherwise the

femtocells are located in different buildings (indoor-outdoor-indoor channel).⇒ If an indoor MS@MBS have a distance to a HBS less than 6m, the MS is located in the same building like the HBS

(indoor channel). Otherwise the MS is located in another building (indoor-outdoor-indoor channel).It is possible that an indoor MS@MBS is located in a building with multiple HBS.

⇒ An outdoor MS@MBS is always outside of any building.⇒ An indoor MS@HBS is always inside the femtocell.

Home NB parameter optimization

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y in

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MBS

Indoor MS@MBS

HBS

MS@HBS

Slide 10

E3


Inter-cell Interference Co-ordination (ICIC) based on real network scenario

ICIC

ICIC: fixed reuse of resourcesfor cell-edge users

SON-ICIC: adaptationof cell-edge resourcesto traffic

Gain in SNIR

Slide 11

E3


Self-x selection of algorithms and first evaluation results

Knowledge based reconfiguration

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Video Streaming Brow sing

Services

Sess

ions

per

cent

age

(%)

Have we addressed thecontext before?

Dynamic Sub-carrier Allocation (DSA) algorithm indicative results

Matching probability

MeanOptimization

Delay

512 Kbps

384 Kbps

128 Kbps

64 Kbps

Higher QoSlevels

assignment

Slide 12

E3


Dynamic spectrum management (DSM) to achieve an efficient utilisation of the scarce and valuable spectrum resources:⇒ Maximise spectrum reuse amongst users, cells, radio access

networks (RAN’s) and systems⇒ Ensuring that mutual interference between them remains at

acceptable levels at the same timeOptimization methodologies covered in E3:⇒ Machine learning,⇒ Genetic algorithms,⇒ Simulated annealing,⇒ Heuristics, etc.

DSM

Slide 13

E3


Multicell OFDMAFixed spectrumassignment strategies comparedwith DSA algorithmsHeuristic Algorithmsand Reinforcement Learning (RL)Algorithms are proposedDSA algorithms improve spectral efficiency, user’s QoSand spatial spectrum usage.First static results show that RL is suitable for DSA

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100(a) Average Dissatisfaction

%

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3.8

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(b) Average Spectral Efficiency

Number of users in scenario

bits

/s/H

z/ce

ll

FRF1FRF3DSA-heurRL-DSA

FRF1FRF3DSA-heurRL-DSA

Dynamic spectrum mangement - example

Slide 14

E3


Increasing processing power and flexibility leads to enhanced radio resource management concepts

Optimized operability and optimized usage of resources of radio access technologies is in focus of future systems

Requirements and concepts for self-x were developed and are under investigation by means of simulation of algorithms

Derivation of recommendations for advanced RRM including performance analysis of different algorithms

Simulation of further operator use cases - HO parameter optimization & load balancing - : “Rule-based algorithms for self-x functionalities in radio access networks”, Rosenberger, M. et al., ICT Mobile Summit Santander

Conclusions and outlook

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09062009 Simulation of self-x algorithms - FP7 SOCRATES workshop Santander... · Common assumptions...

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