xacp 2g automatic cell planning

1 CUSTOMER CONFIDENTIAL

Optimi’s xACP 2GAUTOMATIC CELL

PLANNING

May 2009


Optimi Product Portfolio 2009

3 CUSTOMER CONFIDENTIAL3

• What is xACP?• xACP is a solution that optimizes the RF environment

(for 2G):– RSSI Coverage– Quality (C/I) – Dominance– Capacity

• By potentially suggesting one or more of the following changes (on a sector by sector basis):– PA Power– Antenna type– Azimuth – Mechanical tilt– Electric tilt – Antenna height

• ACP = Optimization Agent + Scoring Mechanism

Automatic Cell Planning


Some of xACP main applications

• Greenfield RF planning with minimum Capex expenditure

• Overlay of new technologies (e.g. WCDMA, LTE, WiMax) on top of existing 2G network reusing existing sites

• Optimization of RF environment during/after site consolidations exercises

• Capex savings in network growth scenarios• Coverage/Quality/Capacity optimization in

existing networks


xACP advanced modeling of network KPI

xACP cost function

Coverage Capacity

Quality0 1 0 1 0 1

• xACP cost function allows the user to balance coverage, quality and capacity effectively, both for uplink and downlink.

• Capacity is a key KPI that xACP accounts for– Coverage and Quality/Dominance issues can be solved at the

expense of creating boomers, which will reduce the capacity of 2G and 3G systems. xACP avoid this by producing the best network design that improves coverage, quality & capacity


Automatic Cell Planning process flow

GIS & clutter Data

TrafficDemand

Path LossData

Scan DriveData

Optimized Cell Site Configuration

xACP

AntennaPatterns

User-defined Optimization Weightsand Settings

Coverage/Quality Metrics

Cell Site

Configuration

UL/DL Loading

Simulation / Measured Data

OSS Counters


2G xACP modules

Optimi xACP is an Optimization suite that includes Optimization and Simulation modules:

• ACP module optimizes the RF parameters (Antenna configuration & Power Settings) of the network by balancing coverage, quality and capacity while taking into account scheduling, and financial considerations

• OSS Based Solution offers a unique approach that integrates OSS counters to compensate path loss errors. Eliminates need to have accurate prediction models as a required input. OSS counters also provide loading conditions.

• Site Selection module is used for designing new networks or decommissioning sites of existing network by selecting the minimum number of sites needed to meet a target design service level for mix of services taking into account Capacity and HR/FR distribution

• Neighbor List Optimization module that optimizes the neighbor list after ACP run that will change the coverage footprint of the sectors

• 2G Monte Carlo Static Simulator that predicts the QoS for GSM


ACP Evolution: From Theory to Practice

Purely predictions based ACP

• Very vulnerable to database errors and prediction inaccuracy

Interpolation of predictions and DT

• Extra accuracy and robustness against database errors

High Scalability

• Applicable to the whole network• Easy to use• Realistic work order

OSS Assisted ACP

• Extra accuracy from real network data.• Traffic maps from OSS data

OSS based ACP

• No need for Drive Test• Robust against database errors


Drive Data Smoothing

• Smoothing Target: Improving predictions in non-driven bins using drive data

• Interpolate the error between drive and prediction

• Interpolation must be practical• Mathematical interpolation is not

enough• Driven bins averaged with different

weights depending on the distance to the bin which is being smoothed

• Propagation Smoothing using drive data runs in minutes and not hours

Drive Data

Propagation


Smoothing Example

Best server before smoothing Best server after smoothing

Best server before smoothing

with drive measurements


• xACP generates a schedule of changes to optimize ROI and considering budget

• Changes grouped & sorted in order of importance in terms of performance improvement– In this way, the operator can decide the point at which it is not

worth to keep on updating the network any longer.

xACP realistic work order


• Structured in such a way that a soft and safe transition path is provided, making sure that temporary network quality problems due to intermediate states associated with long lasting update operations are minimized :– Unlike frequency plans, RF plans cannot be implemented at

once in a short period of time

xACP realistic work order


Site Selection

Several usage cases:- Initial technology rollouts (UMTS, WiMAX, LTE...)- Two commercial network consolidation processes to select the optimal subset

of sites- UMTS overlay over existing GSM site base- Joint xACP optimization plus site additions.

Anchored Network

Final Design

• Given a set of site candidates to be decommissioned, it selects the optimal subset of candidates that fulfils a performance target

• Given a set of site candidates to be deployed, it selects the optimal subset that meet the performance targets

CandidatesSite removed / added


High Scalability

• xACP runs on large projects without artificially splitting them– Splitting the projects increases the complexity of managing ACP

projects and analyzing the results– The project cannot be easily divided into sub projects. No

overlap between projects will yield a design that neglects interference from neighboring project, large overlap yields to increase in ACP run time

• xACP runs on state wide projects (400km*400km) at high resolution (30m) in hours and not days!– Engineer can execute the ACP run overnight so that results are

available in the morning to be analyzed– Medium Size markets run in 3-4 hours


• Missing neighbors are one of the main reasons for dropped calls• New neighbor list has to be implemented after ACP run as the best

server coverage changes• Neighbor list optimization module allows the users to automatically

create new neighbor list• It allows the user to define neighbors based on overlap, co-site,

distance etc.• OSS neighbor measurements can be imported for extra accuracy

Neighbor List Optimization


OSS Based xACP concept


Fundamental inputs to ACP:1. Physical information

– LAT, LON for every site– RF configuration for every sector– Transmission powers

2. Raster data– Elevation maps– Clutter data– Propagation predictions– Traffic maps

3. Environment related information– Indoor & clutter penetration losses– Thermal noise level

4. Load factors– DL traffic channel power– Uplink NR

ACP input data


Fundamental inputs to ACP:1. Physical information

– LAT, LON for every site– RF configuration for every sector– Transmission powers

2. Raster data– Elevation maps– Clutter data– Propagation predictions– Traffic maps

3. Environment related information– Indoor penetration losses– Thermal noise level

4. Load factors– DL traffic channel power– Uplink NR

Usually based on estimations and/or inaccurate

assumptions

If taken from OSS counters Boost accuracy

Cost effective: no probes, etc.

OSS based ACP input data


ACP process flow

User-defined Optimization Objectives and Settings


Cell Site

Config Elev & clutter

Path Loss (pred, DT)

AntennaPatterns

xACP optimization engine


xACP

Planning tool

OSS

UL/DL Loading

TrafficMap

xACP Simulation engine


OSS based ACP process flow

User-defined Optimization Objectives and Settings


Cell Site

Config Elev & clutter

Path Loss (pred, DT)

AntennaPatterns

xACP optimization engine


xACP

Planning tool

OSS KPIS

OSS

Traffic

xACP Simulation engine

DL/UL LoadTraffic Map


2G OSS Interface• Import Interference Matrix and RxLev-RxQual data (from xAFP project)


2G OSS Interface

• Import Time delay information (timing advance)

• Adjust Coverage and interference:


OSS based traffic map generation

• Create a demand grid traffic with sector specific traffic from OSS– Using polygons to spread the

traffic– Using clutter weights– Using prop delay information

With timing information

Without timing information

More realistic traffic maps


Dominance View

Dominance Raster view

Dominance Delta view(predictions vs OSS)


• It is well-known the propagation predictions may contain inaccuracies that, if severe, can seriously jeopardize the entire optimization process.

• Statistical KPIs from OSS are used to better match the measured data and thus, user experience.

• Better match RSSI holes and C/I or dominance problems

• Main advantages of OSS based ACP are therefore two-fold:• Reduction or even elimination of the need for drive tests• Possibility to capture the real user experienced radio

conditions (indoor losses, traffic distributions)

Accurate RSSI and C/I rasters


Coverage holes detected after evaluating raw predictions with a threshold of -80dBm

Coverage holes detected with OSS based ACP

Accurate RSSI and C/I rasters


xAFP and 2G OSS-based xACP

OSS stats

Antenna changesSector 1 ET 1 → 2Sector 4 ET 3 → 1Sector 5 ET 4 → 6

xAFP OSS based ACP

QoS baseline QoS after ACP QoS after AFP

2G simulator

Scaled OSS stats New frequency plan+ NL optimization

OSS stats

xAFP


Automated scaling of IM and RxLev data for AFP execution after OSS based ACP

• Update RxLev histogram per cell • Generate IM from scaled predictions


Basic 2G ACP OSS based case: Live Network Optimization

in a European CityJuly 2008


OSS Based ACP Example: Pilot Europe

• 2G optimization, Siemens:• AZI and M-TILT were optimized with 2G OSS based ACP

• Schedule of changes sorted by importance and sites was provided• Neighbor list optimization also done

• Retune1 : part of MMR collection after physical changes• Retune2 : MMR collected after physical changes implemented


2G performance analysis based on OSS KPIs Improvements table

KPI Optimi Baseline GainCS traffic (Erl) 8668 Erl 7932 Erl 9 %

CS Call Drop Rate (%) 0.36% 0.41% 16 %

Good DL Quality Share (%) 96.14% 95.50% 14 %

Good UL Quality Share (%) 97.02% 96.21% 20 %

Bad DL Quality Share (%) 2.90% 3.37% 14 %

Bad UL Quality Share (%) 2.01% 2.66% 23 %

HO Best Cell Share (%) 89.02% 87.49% 15 %

HO DL Quality Share (%) 4.62% 5.54% 22 %

HO UL Quality Share (%) 3.71% 4.26% 15 %

• Overall 2G performance improvement was perceived. Quality improvement comes from dominance and C/I cleaning which was achieved with azimuth and M-Tilt optimization.


Summary

• xACP is a key optimization solution for mobile operators and will be even more in the future with the introduction of new technologies,

• Optimi’s xACP goes beyond optimizing a design and showing improvements in a planning tool.

• The advanced interfaces and feature set included in xACP makes it possible to integrate it smoothly in any operators process.

• xACP uses advanced modeling and innovative sources of information for extra accuracy and better usability. This allows xACP to be a fully operational tool without even requiring drive test data.

xacp 2g automatic cell planning

Documents

defined optimization

coverage holes

site rf configuration

oss based

physical information

acp run

accurate rssi

extra accuracy