selfx wiopt09 barth - semantic scholar · pdf fileall rightsreserved©alcatel-lucent 2009...

Self-X RAN

Autonomous Self Organizing Radio Access Networks

Bell Labs Stuttgart

Ulrich Barth

June 2009

All Rights Reserved © Alcatel-Lucent 2009 2

Self-X Business Perspective /

Bell Labs SON vision


Self- organizing Radio Access Networks

Motivation

Current situation for radio access network management

� Deployment and maintenance become more and more complex and costextensive

� Trend to smaller cells, multi-band operation, heterogeneous mobile networks

� High manual intervention for configuration, capacity upgrade or in failure cases required

� High effort required for optimisation of system performance

� Deep system expertise required

� High effort necessary for measurement campaigns (drive tests)

� Different tools for planning, configuration, measurement/KPI acquisition and optimisation involved

increasing effort for network management and optimisation

�� new concepts for simplified network operation required


Self-X Architecture

� “NEM less” network management

� Fully autonomous, distributed

RAN optimisation

� Self-x functions in UE and eNB

� measurements, UE location info

� alarms, status reports, KPIs

� distributed self-x algorithms

� Network management in NM OSS

focussed on

� network planning

� alarm and performance monitoring

� high level performance tuning

Vision of fully distributed self-management

eNB

LTE RAN

Network Management

eNB

eNB

self-x

NM OSS

Itf-N

X2-Itf

self-x

self-x

RAN self-optimization

�� performance monitoring

�� KPIs

�� alarms

�� high level network

performance tuning

OSS: Operation Support SystemNEM: Network Element Manager


� RAN configuration use cases:

– Add/Remove cell incl. power saving cell

– Neighborhood relation configuration and optimisation for LTE

� RAN optimization use cases

– Cell coverage optimization

– Mobility robustness optimisation

– Interference optimisation for LTE

– Load Balancing

� QoS optimization use cases

– Scheduler operation optimisation for LTE

– MIMO mode selection optimisation for LTE

Self-Organizing Radio Access Network

deployment new site,

add new cell, capacity upgrade

self-configuration

performance optimisation

self-optimisation

tools for RAN planning,

configuration

and optimisation

conventional parameter

configuration

failure cases


Self-Configuration of Radio parameters


Self-configuration of eNB Radio Parameters:

Add Cell Use Case

Automatic Self-Configuration of Radio Parameters

� deployment/removal of cells/sites

� switching on/off of cells

Vision: fully autonomous plug’n play

� finding similar neighbors

� learning optimized configuration

from similar neighbor eNBs/cells

� calculation, adaptation and

negotiation of parameters

� distributed approach

� based on

� parameter classification

� parameter calculation

� similarity metrics

� configuration management

Parameter Retrieval

Config. Parameter Calculation Operational Phase

operatortemplates only for:

enablingnew features

preferences

initial defaults

parameter adaptation

negotiations with neighbours

outlier filter

self-optimisation

self-configuration

config-parameter classification

learning fromsimilar neighbours

neighbour selection: similarity metric

classificationown properties

and environment


Self-configuration of eNB Radio Parameters:

Add Cell Use Case

What is and how to select a suitable neighbor?

� geographical proximity

� similarity of HW, cell properties (macro, micro, …; power class; …), environment

� parameter group wise retrieval from different eNBs (eNBs with different properties)

� similarity metrics:

based on

� vector representation of relevant parameters with weighting factors:

vector norm based identification of similarity (e.g. Euclidean distance)

Learning and storing good (optimized) configurations:

some optimized parameter sets depend e.g. on time and date, load

� for use in restart situations

� for distinguishing different optimized configurations (e.g. load dependent)

� recognition of parameter clustering

� cluster wise saving of configuration parameter sets

� cluster dependent reload of configuration data

l1

l2

mdmdm p

iiii BAWC )(, Θ•=C: distance measure, W: weights

A: current node, B: neighbor

Θ: generalized difference


Automatic Neighbour Relation


Automatic Neighbour Relation Function (ANR)

W-CDMA needs NRT for UE measurements

� UE are configured by NodeB which

cell to be measured (e.g. for HO)

� Centralized NRT planning required

No such restriction in LTE

� all UEs can measure the Physical Cell

ID (PCI) of all neighbours

� eNB can request the UE to measure

the Cell Global ID (CGI) related to

the PCI

� PCI/CGI is the key info needed in

NRT to map it further to the IP

address of eNB

� X2 Setup between the eNBs to enable

handover

UE

eNB

NeighboreNB

X2

NeighboreNB

SON ANRalgorithm

Neighbour Relationship Table

(NRT) per cell

Cell A

Phy CID 3

Cell Global ID 17

Cell B

Phy CID 5

Cell Global ID 19

Report Phy CID 5 Strong Signal

up to 15 eNBs


Automatic Neighbour Relation Function (ANR)

Bell Labs decentralized proposal for ANR

� Start with empty NRT list

� Generation of NRT only based on UE measurements

� Update/fine tuning based on handover optimisation

� Detection and correction of PCI collision/based on ANR

Simulation Assumption for feasibility study

� Measure Convergence Time and HO failure in worst case scenario

� Only information from HO signalling is used

� No additional measurements used

� No signalling with neighbour cells

� Full radio simulation

12

34

5

612

34

5

612

34

5

612

34

5

6


NRT Simulation (Hexagonal Grid layout 57 cells)

Inter Site Distance = 500 m95% Quantile of the NRT Completion Time

0100200300400500600700800900

10001100120013001400150016001700

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6No. of UEs Per Cell

Tim

e [s

ec]

3 km/h 30 km/h 120 km/h

NRT list setup only based on UE measurement feasible

� Convergence time sufficiently short

� Worst case scenario simulated, as only UEs in handover process participate to

NRT

HO Drops Due to Incomplete NRT

0102030405060708090

100

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6No. of UEs Per Cell

HO

Dro

p [

%]

3 km/h 30 km/h 120 km/h


SON: Autonomous Coloring Algorithm for

Frequency assignment


Autonomous Coloring Algorithm for Frequency assignment

P

f1 2 3 4 5 6 7

P

f1 2 3 4 5 6 7

Inter-Cell Interference Coordination

Self configuring and optimizing Network

Hand Over failure reduced by 5 fold

Increased the throughput up to 27%

Performance increase in call set up

Improve performance at cell edge

Self-organizing pattern assignment


Inter-Cell Interference Coordination (ICIC) on terminal mobility

Pfull

P

f

Pfull

P

f1 2 3 4 5 6 7

1 2 3 4 5 6 7

a

b

d

e

c

Frequency Patterngreen cella. Mobile is scheduled to sub-band 3

with negligible interference from

orange cell

b. Mobile is scheduled to sub-band 2,

where orange cell radiates with

lowered power

c. Mobile is handovered

from green cell to orange cell

d. Mobile is scheduled to sub-band 4,

where green cell radiates with

lowered power

e. Mobile is scheduled to sub-band 3

with negligible interference from

cell 1

Frequency Patternorange cell

ab

c

de


Autonomous Coloring Algorithm for Frequency assignment

Motivation

• Bell Labs ICIC approach requires frequency planning

But frequency planning is OPEX consuming

• � Provide a self-organizing solution

for cell (sub-)frequency (‘colour’) assignment

Challenges and � Bell Labs Solutions

• Known mathematical approaches are only centralized ...

� Fully distributed colouring algorithm inside each eNB

• ... and require much too much computation effort for real networks

� Efficient solution inside restricted areas by a novel successive algorithm

• Existing approaches are not adapted to the radio networks

� KPI for algorithm based on Interferences and n-tier neighbours

� Best suited colour solution found – also when a perfect one does not exist

• Decentralized systems can be susceptible to ‘instabilities’

� Advanced mechanisms to detect and resolve oscillation effects

� Advanced functionality to avoid “a moving wave of changes through the network”


Major Steps of the Self Organizing + Self Optimizing SON Algorithm

Fast Initial Colouring:Each cell colours itself - if possible� ICIC immediately operational

Local Area Colour Optimization:Optimizing the colour assignment for several cells� Resolving sub-optimal neighbour colour assignments � Finding the optimal interference situation � Several advanced mechanisms to prevent instabilities� ...

Neighbour Relation Table (NRT) sufficiently filled� Scenario Creation / Update inside the eNB

Self Adaptation: Add/Drop Cell,NRT Change

Periodicoptimiza-tion by each cell

- Algorithm + signalling 3GPP compliant (i.e. LTE Rel.8) - Fully distributed algorithm, runs inside each eNB


Operation of SON ICIC algorithm

Initial eNB based (self-) assignment of

frequency patterns for ICIC

� network is already in operational state

without lowered sub-bands (i.e. re-use 1”

― no frequency pattern is assigned)

� self-assignment is started when the NRT

has settled after ANR

� the found assignment is stable while the

particular NRTs do not change significantly



Modification of network deployment

� Addition of Omni-directional cell

� Initial color is chosen to the fewest

interference load (best neighbour)

� Subsequent optimization procedure finds a

solution by re-coloring the own cell and a

further (neighbour) cell



Modification of network deployment

� Replacement of Omni-directional cell with

tri-sectorized basestation

� Quick reaction of neighbors on changed

neighborhood in NRT


Mobility Robustness (Handover Optimization)


Configuration Parameters for Handover in LTE

LTE handover more sensitive compared to W-CDMA

� Configuration parameters

� Filtered RSRP values

� Handover Margin, i.e. hysteresis between source and target

� Time to trigger (TTT)

� Cell Individual Offset (CIO)

TTT (ms)

FilteredRSRP[dB]

Source CellTarget Cell

TimeHandoverCommand

Hyst(dB)

HandoverEvent A3

P(ms)

RLF threshold

Radio problemdetection

T1 (e.g. 500 ms) Radio link failure

TTT (ms)

FilteredRSRP[dB]

Source CellTarget Cell

TimeHandoverCommand

Hyst(dB)

HandoverEvent A3

P(ms)

RLF threshold

Radio problemdetection

T1 (e.g. 500 ms) Radio link failure


Targets For Self-Optimization of Handovers (HO)

� To increase network performance by the minimization of Radio Link Failures

(RLF) and ping pong effects occurring due to inappropriate HO parameters

� To avoid manual update and setting of HO parameters after the initial

deployment

� To monitor neighbor specific HO problems

� Each cell monitors the HO problems occurring due to its own parameters or due to

specific neighbor’s parameters

� Every cell autonomously detects and resolves the HO problems by using

decentralized self-detection and optimization algorithms

� To avoid drive tests run specially for the detection of such problems


Classification of HO Problems

RLF due to inappropriate HO decisions and HO parameter settings

� RLF before HO

� RLF before source cell receives UE measurement report for initiation of HO

� detection by source or neighbor cells

� RLF during HO

� RLF in source cell occurring during HO (HO command failure)

� detection by source or neighbor cells

� RLF just after HO

� RLF in target cell just after the successful HO

� detection by target cell

Short Stays

� Ping pong effect

� Rapid handovers between two neighbor cells

� Island effect

� Handover from Cell A to Cell C and successive rapid handover from cell C to Cell B instead of handover directly from Cell A to Cell B (avoid short stay in Cell C so called hot spot or island effect)


Possible Handover Optimization

Avoiding high handover failure rates or too many short stays

� Detection of non-suitable neighbor relations by collecting and

analyzing handover statistics

� Optimization algorithms have to deal with rare and sporadic input values

� Avoid handovers to non-suitable neighbors

� Considering that in some cases only

specific locations at cell borders are

non-suitable


Possible Handover Optimization

Optimization by modification of HO parameters

� Make sure handover problems are caused within the source cell

� Options for modification of HO parameters in source cell

� Handover Margin (HOM)

� Time to Trigger HO (TTT)

� Filter Coefficient and Cell Individual Offset (CIO)

� Simulation results

� HOM and Filter Coefficient can be fixed

� TTT must be selected depending

upon the UE speed

Normalized HO Rate Vs Residual BLER for ; TTT=0 to 200 ms; 20ms step

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10

BLER [%]

No

rmal

ized

HO

Rat

e


Coverage and Capacity Optimisation


know how shiftfrom OAM expert

to manufactureroptimization algo design

Coverage Optimization for LTETargets

� detection and minimization of coverage & capacity problems

� load / UE density depending tilting

� cell outage compensation & power saving by switching cells off/on

Vision

� after planning and deployment of a new cell:

� fully automatic / autonomous optimization in eNB: antenna tilt, TxPower

� replacement of drive tests

� decentralized / distributed approach

New optimization process required:

cell global PM counters

drive tests, UE

call based traces

root cause analysispartly automated, expert driven

(planning) tool based re-planning

expert know how


ce

ntr

ali

ze

d:

off

lin

e,

too

l an

d e

xp

ert

base

d

UE measurementsUE location info

cell global PM counters

automatic measurement configuration, data evaluation

optimization algorithm


de

ce

ntra

lize

d:

co

ntin

uo

us,

op

timiz

atio

n a

lgo

rithm

base

d

alg

orith

m d

esig

n

⇒⇒⇒⇒

STATE OF THE ART SON TARGET


Coverage Optimization for LTE

Challenges:

� complex optimization problem:

� collaborative (w.r.t cells and sites) and predictive optimization required

� interdependency with other self-x/SON optimization targets

(e.g. HO optimization, load balancing)

� spatially resolved detection based on UE measurements required:

� areas with insufficient coverage / low SINR / high interference

� areas with high traffic (hot zones)

� limitations/constraints regarding UE based measurements:

� accuracy, range and availability (radio link based and positioning data)

� statistical nature

� adaptation to network dynamics

� mid and long term changes in traffic load/distribution, interference,

neighbor relations


Outage Compensation

Cell outage compensation by

� power variation

� no real compensation by power

reduction of neighbours

� power increase: drawback

large over provisioning required

� azimuth variation

� good compensation results (almost complete coverage)

but: normally not available in the field

� antenna tilting

� at least partial compensation expected



Impact of tilt:

CDF of Geometry reflects situation

in entire simulated area.

� Example with various tilt angles

9-21 degrees, 15 degrees provide

optimum coverage.

Simulation model:

� channel model: Okumura Hata,

shadow fading 10dB std dev.

� SINR: serving cell selection by strongest signal,

interference: sum of all remaining cells

� interference limited

� 500m inter site distance

coverageproblems

15°°°° 18°°°° 21°°°°12°°°°09°°°°



Optimisation goals:

� optimize CDF especially for low geometry values

� view: cell global

� - 3dB Problem of 3-sectorised base stations with re-use 1:

locations where 3 sectors have almost the same signal strength

� local problem, put in areas of very low user density

� discrete coverage hole:

� local geometry optimization problem with high relevance

� user density/ load:

� conditional probability distributions can be employed:

e.g. exclude locations w/o users, there is no need to provide coverage at all

� optimize geometry in high traffic zones


Load Balancing


Load Balancing

based on HO parameter modification:

� LTE intra frequency handover

� critical in re-use 1 schemes:

– no scrambling gain

– lower limit for usable SINR range

– especially critical: HO command

� potential for load balancing rather low

� LTE inter frequency HO

� no cell overlap SINR problem

– e.g. hierarchical cell structures

– to be considered: UE velocity vs. cell size, QoS requirements (e.g. GBR, NGBR)

� load balancing possible

� Inter system HO

� also no cell overlap SINR problem

– to be considered: service QoS requirements

� load balancing possible

�

�

�

with static ICIC, reuse 7/6

00,020,040,060,080,1

0,120,140,160,180,2

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5

HO

Rat

e [1

/s]

TTTH=0.050 sec

TTTH=0.100 sec

TTTH=0.150 sec

without ICIC

00,020,040,060,08

0,10,120,140,160,18

0,2

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5BLER [%]

HO

Rat

e [1

/s]

TTTH=0.050 sec

TTTH=0.100 sec

TTTH=0.150 sec

Residual BLER [%] (RLF)

Residual BLER [%] (RLF)

w/o ICIC

with ICIC


Load Balancing

other approaches for intra frequency LTE:

� DL Power modification

� increased power in unloaded neighbour cells:

– requires PA over provisioning

– UL critical

� decreased power in overloaded cell:

– possible in interference limited (urban) scenarios

– degrading indoor coverage to be investigated

– risk of local coverage spots

�� ongoing investigation

� Interference coordination enabled load balancing:

IFCO as Enabler

� dynamic allocation of subbands for reduced power

� load reduction by dynamic IFCO based interference reduction

� seems to have higher potential, ongoing investigation

1 2 3 4 5 6 7

1 2 3 4 5 6 7


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selfx wiopt09 barth - semantic scholar · pdf fileall rightsreserved©alcatel-lucent 2009...

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