WiOpt09_Speech3

8/8/2019 WiOpt09_Speech3

1/36

Self-X RAN

Autonomous Self Organizing Radio Access Networks

Bell Labs Stuttgart

Ulrich Barth

June 2009


2/36

All Rights Reserved Alcatel-Lucent 2009 2

Self-X Business Perspective /Bell Labs SON vision


3/36


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 casesrequired

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


4/36


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

performancemonitoring

KPIs

alarms

high level network

performance tuning

OSS: Operation Support System

NEM: Network Element Manager


5/36


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

deploymentnew site,

add new cell,capacityupgrade

self-configuration

performanceoptimisation

self-optimisation

tools for RANplanning,

configuration

andoptimisation

conventionalparameter

configuration

failure cases


6/36


Self-Configuration of Radio parameters


7/36


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 plugn play finding similar neighbors

learning optimized configuration

from similar neighbor eNBs/cells

calculation, adaptation andnegotiation of parameters

distributed approach

based on

parameter classification parameter calculation

similarity metrics

configuration management

Parameter Retrieval

Config. Parameter Calculation Operational Phase

operatortemplatesonly for:

enablingnew features

preferences

initial defaults

parameter adaptation

negotiations with neighbours

outlier filter

self-optimisation

self-configuration

config-parameterclassification

learning fromsimilar neighbours

neighbour selection:similarity metric

classificationown properties

and environment


8/36


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 piiii BAWC )(, =

C: distance measure, W: weightsA: current node, B: neighbor

: generalized difference


9/36


Automatic Neighbour Relation


10/36


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 requiredNo such restriction in LTE

all UEs can measure the Physical Cell

ID (PCI) of all neighbours

eNB can request the UE to measurethe 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


11/36


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

1

2

34

5

61

2

34

5

61

2

34

5

61

2

34

5

6


12/36


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 6

No. of UEs Per Cell

Time[sec]

3 km/h30 km/h120 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 toNRT

HO Drops Due to Incomplete NRT

0

10

20

30

40

50

60

70

80

90

100

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

No. of UEs Per Cell

HODrop

[%]

3 km/h

30 km/h

120 km/h


13/36


SON: Autonomous Coloring Algorithm for

Frequency assignment


14/36


Autonomous Coloring Algorithm for Frequency assignment

P

f

1 2 3 4 5 6 7P

f1 2 3 4 5 6 7

Inter-Cell InterferenceCoordination

Self configuring andoptimizing Network

Hand Over failure reduced by 5 fold

Increased the throughput up to 27%

Performance increase in call set up

Improve performanceat cell edge

Self-organizingpattern assignment


15/36


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


16/36


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


17/36


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


18/36


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


19/36



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


20/36



Modification of network deployment

Replacement of Omni-directional cell with

tri-sectorized basestation

Quick reaction of neighbors on changed

neighborhood in NRT


21/36


Mobility Robustness (Handover Optimization)


22/36


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 Cell

Target 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 Cell

Target Cell

TimeHandoverCommand

Hyst(dB)

HandoverEvent A3

P(ms)

RLF threshold

Radio problemdetection

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


23/36


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 initialdeployment

To monitor neighbor specific HO problems

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


24/36


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 Binstead of handover directly from Cell A to Cell B (avoid short stay in Cell C so calledhot spot or island effect)


25/36


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


26/36


27/36


Coverage and Capacity Optimisation


28/36


know how shiftfrom OAM expert

to manufactureroptimizationalgo design

Coverage Optimization for LTE

Targets 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 globalPM counters

drive tests, UE

call based traces

root cause analysispartly automated, expert driven

(planning) tool based re-planning

expert know how


centralized:

offline,

tooland

expert

based

UE measurementsUE location info

cell globalPM counters

automatic measurementconfiguration,

data evaluation

optimization algorithm


decentralized:

c

ontinuous,

optimizationalgorithmbased

algori

thmdesign

STATE OF THE ART SON TARGET


29/36



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


30/36


Outage Compensation

Cell outage compensation by

power variation

no real compensation by powerreduction 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


31/36



Impact of tilt:

CDF of Geometry reflects situation

in entire simulated area.

Example with various tilt angles

9-21 degrees, 15 degrees provideoptimum 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 211209


32/36



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


33/36


Load Balancing


34/36


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

00,020,040,060,08

0,10,12

0,140,160,18

0,2

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

HORate[1/s]

TTTH=0.050 sec

TTTH=0.100 sec

TTTH=0.150 sec

00,020,040,06

0,080,1

0,120,140,160,18

0,2

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

HORate[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


35/36


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


36/36


www.alcatel-lucent.comwww.alcatel-lucent.com

WiOpt09_Speech3

Documents

Transcript of WiOpt09_Speech3