Fracton tarec in offerings intro

Fracton Technologies

…Piecing It All Together!

Authorized Partner

A privately held company, headquartered in India.

One of the first companies in the country to develop RF Optimization Tools.

Outstanding track record in offering Optimization Services to the leading operators.

Proud winner of 2015 Graham Bell Award for Best Telecom Product.

About Us

The Company

The Team

The team possesses world class expertise in –

Planning & Management of Mobile Networks.

Design & Development of Telecom Products.

A fairly young, motivated and highly agile team.

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Network Services Offered

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GSM/UMTS Network Parametric Optimization

Radio Access Network Audit

Automatic Frequency Planning

Automatic PSC Planning

Spectrum Refarming

The Cellular Symphony

MaxCell – Core Philosophy

Each Cell is Unique!

So are each of its neighbor relationships!

Why to condemn them to a herd culture,

then?

By leaving their critical parameters at their default levels!

Respect their individuality, instead!

Give them what they ask for… and allow your

network to perform at its best!

5

An OSS based RAN Optimization Tool.

Addresses the Uniqueness of each Cell &

Neighbour.

Optimizes Network Performance & Quality of

Experience in a Flash! Reduction in Customer Complaints

Reduction in Call Drops

Reduction in Handover Failures

Reduction in iRAT Handover Failures

Improvement in Voice Quality

Improvement in Data Throughput

Enables a Dynamic & Continuous Network

Optimization Regime.

Significant Capex & Opex Reduction.

Multi-vendor, Multi-technology Solution.

MaxCell - Introduction

6

Pre-Post KPI

Reports

OSS/NMS

Operator’s Configuration

Management

System

Operator’s Performance

Management

System

Performance Data

Existing Configuration

MR/Trace Data/ Traffic Recordings

Recommended Configuration

Traditional Way

Default Cell DB Parameters Set at Network Level

No Performance Gains

Manual Way

Optimized Cell DB Parameters on RNC/BSC or Cluster Level

Marginal Performance Gains

Automated Way

MaxCell Optimizes Per Cell/Per Neighbor Cell DB Parameters

Decisions Based on Radio Network Configuration & Performance Counters, KPIs & MRR Reports

Significant Performance Gains

7

Why MaxCell ?

Automated Way of Cell Database Parameters Tuning

MaxCell Multi-Vendor & Multi-Technology Automated Solution

• Optimization Concept – Optimizes Key RRM Parameters at Sector, Carrier,

Frequency Level & Per Neighbor Level, thereby Enhancing System performance while minimizing CapEx and OpEx

– Designed to capture detailed RF Data from operators’ existing system and fully automates the overall parameter optimization process

• Main Focus Areas – MaxCell’s Functionalities

• GIS Based Visualization • Performance Reporting & Charting Capabilities • Optimization Algorithms

– 2G Optimization Module – 3G Optimization Module – 4G Optimization Module

MaxCell solution enables a Per Sector, Per Carrier & Per Neighbor Optimum Parameters Tuning to Maximize

the Overall Network Performance, in a way that is Virtually Impossible to achieve Manually.

Every Cell & Every Neighbor Relation is Unique

….. as such requires Optimum Parameter Settings

Strictly Confidential 9

Note: The “LTE Key Features”, functionality is being tested. Rest of the Modules are fully operational

MaxCell-4G Optimization Cycle

MaxCell Inputs

Optimization Targets

Optimization Engine

Parameters Implementation

Performance Assessment

RSRP & Ant Para

Optimization

•Coverage Optimization

•No Dominance Optimization

•Pilot Pollution Optimization

•SINR & CQI Optimization

RF Performan

ce Optimizati

on

•Intra/Inter Frequency Cell Re-selection

•Inter RAT Cell Re-selection

•Inter RAT & Inter Frequency HO

•Intra Frequency HO Parameters

•DL & UL Throughput

LTE Key Features

•MIMO

•CSFB & SRVCC

•HetNet

Neighbor List

•PCI Optimization

•Intra & Inter Frequency Neighbor Lists

•IRAT Neighbor Lists


Typical Project Schedule – Improvements Observed in a Fortnight

MaxCell-4G Optimization Timelines

Week 1

•Selection of Optimization Area

•Existing Performance & KPI Assessment

•Define Optimization Targets – SINR, CQI, Layers Re-selection Criteria

•Schedule Traffic Recordings – MR & CQI

Week 2

•Import Network Configuration

•Import CM, PM & MR/Trace Recordings Data

•Run MaxCell Optimizer

•Generate Antenna Conf. & Cell Database Parameters Tuning Recommendations

Week 3-4

•Iteration 1a: Ant Conf & RSRP Power.

•Performance Data Collection for Iterations 1b, 1c & 1d – MR/Trace

•Iteration 1b: Intra Frequency & Inter RAT Cell Re-selection Optimization

•Iteration 1c: Intra Frequency & Inter RAT Handover Parameters Optimization

•Iteration 1d: Neighbor List Optimization

•Performance Data Collection for 2nd Iteration – MR/Trace & Counters

•Iteration 2 (Fine Tuning): Recommendations Generation & Implementation

Week 5

•Pre Post Performance Assessment

•Benchmarking

•Project Report Submission

MaxCell 4G Solution for Ericsson Data Input Requirements

• Configuration Dump – OSS XML Dump taken through OSS

Common Explorer

• Performance Counters & KPIs – OSS Counter Cell, Frequency Level & Per

Neighbour Level Stats

– 7 Working Days from 10 am to 10 pm

– NBH & BBH KPI Reports

• Ericsson LTE CTR Recordings – Layer-3 Trace Recordings

• 3 Days & Hourly recordings from 11:00 am to 12:00 pm

Optimization Strategy: Exploiting Performance Counters & Cell Trace Recordings for

Assessing Traffic & Inter Cell behavior which forms the basis for Optimization Algorithms.

MaxCell 4G Solution for Huawei Data Input Requirements






• Huawei LTE Call Trace / PCHR Recording – Layer 3 Cell Trace Recordings


Optimization Strategy: Exploiting Performance Counters & Traces Recordings for


MaxCell 4G Solution for ZTE Data Input Requirements


Common Explorer





• NetMax MR/CDT Recording – MR Recordings


Optimization Strategy: Exploiting Performance Counters & NetMax Recordings for

Assessing Traffic behavior which forms the basis for Optimization Algorithms.

MaxCell 4G Solution for Nokia Data Input Requirements


Common Explorer





• Megamon-Emil Recording – Layer 3 Cell Trace Recordings


Optimization Strategy: Exploiting Performance Counters & Megamon Recordings for

Assessing Traffic behavior which forms the basis for Optimization Algorithms.


MaxCell Solution is Based on Iterative Parameters Tuning Cycles


MaxCell Inputs


Optimization Engine



CPICH & Ant Para

Optimization

•Coverage Optimization

•Pilot Pollution

•No Dominance Areas Optimization

Soft Handovers

•Cell Selection/ReSelection – Intra/Inter Freq

•Soft Handovers Parameters Optimization

•IRAT Cell Reselection & HO Optimization

•Inter Frequency HO Settings

Capacity & Admission

Control

•AC Thresholds

•Max Power Per Link / FACH Power

•HSDPA & DL Power

•Code Allocation

Neighbor List

•Scrambling Code Optimization

•Intra & Inter Frequency Neighbor Lists

•IRAT Neighbor Lists




Week 1



•Define Optimization Targets – Voice Vs HSDPA

•Schedule Traffic Recordings – MR/Traces/Traffic Recording

Week 2


•Import CM, PM & Traffic Recordings Data


•Generate Antenna Conf. & Cell Database Parameter Recommendations

Week 3-4

•Iteration 1a: Ant Conf & CPICH Power.

•Performance Data Collection for Iterations 1b, 1c & 1d – MR/Traces/Traffic Recording

•Iteration 1b: Soft Handover Performance Optimization

•Iteration 1c: Downlink/Uplink Capacity Optimization

•Iteration 1d: Neighbor List Optimization

•Performance Data Collection for 2nd Iteration – MR/Traces/Traffic Recording


Week 5


•Benchmarking


MaxCell 3G Solution for Ericsson Data Input Requirements


Common Explorer • Performance Counters & KPIs

– OSS Path: /var/opt/ericsson/nms_umts_pms_seg/segment1/XML/

– Counter Cell Level Data & Per Neighbor Level – 7 Working Days from 10 am to 10 pm – MO Classes: UtranCell, RncFunction, Hsdsch, Handover,

UtranRelation, GsmRelation – NBH & BBH KPI Reports

• GPEH Traffic Recording – WMRR & WNCS Recordings – GPEH data for RNC with filter

“RRC_Measurement_Reports” • 3 Days Hours Data from 11:00 am to 12:00 pm

Optimization Strategy: Exploiting Performance Counters & GPEH Traffic Recordings for


MaxCell 3G Solution for Huawei Data Input Requirements

• Configuration Dump

– OSS XML Dump taken through OSS





• Huawei CHR / PCHR Recording – Layer 3 Cell Trace Recordings


Optimization Strategy: Exploiting Performance Counters & Trace Recordings for


MaxCell 3G Solution for ZTE Data Input Requirements

• Configuration Dump

– OSS XML Dump taken through OSS





• ZTE Call Trace System (CTS) – Layer 3 Cell Trace Recordings


Optimization Strategy: Exploiting Performance Counters & Trace Recordings for


MaxCell 3G Solution for Nokia Data Input Requirements


Common Explorer • Performance Counters & KPIs

– OSS Counter Cell, Frequency Level & Per Neighbour Level Stats

– 7 Working Days from 10 am to 10 pm – NBH & BBH KPI Reports

• Megamon-Emil /CHR / MR Recordings – Layer 3 Cell Trace Recordings


Optimization Strategy: Exploiting Performance Counters & Megamon Recordings for




MaxCell Solution is Based on Iterative Parameters Tuning Cycles

MaxCell Inputs


Optimization Engine



Frequency Fine Tuning

•RF Path Issues

•Interference Reduction

•Overshooting Cells Optimization

Handovers

•Handovers Parameters Optimization

•Neighbor List Optimization

•QOS Features Optimization

Traffic Balancing/

Capacity

•Dual Band Layers

•Traffic Balancing / Capacity

•QOS Features Optimization

•Hierarchical / IBC Cells Opt

Power Control

•Power Control Parameters Opt

•Interference Control




Week 1



•Define Optimization Targets – DCR, Voice Quality & HOSR

•Schedule Traffic Recordings – MR / Traffic Recording

Week 2


•Import CM, PM & Traffic Recordings Data


•Generate Cell Database Parameter Recommendations

Week 3-4

•Iteration 1a: Freq Plan Fine Tuning & Neighbor List Tuning

•Performance Data Collection for Iterations 1b & 1c – In case Freq Plan Tuning is req.

•Iteration 1b: Traffic Balancing; Capacity Optimization; Dual Band Layers Opt

•Iteration 1c: Handover Performance, Power Control & QOS Features Optimization

•Performance Data Collection for 2nd Iteration – MR/Traffic Recordings & Counters


Week 5


•Benchmarking


Automatic Frequency Planning

AFP - Introduction


A Multi-Vendor Automatic Frequency Planning

Based on Mobile Measurement Reports Captured

Improves Spectral Efficiency & Capacity

Optimizes Network Performance KPIs Drop Call Rate

Voice Quality (RxQual)

Handover Success Rate

TCH Assignment Success Rate

AFP Cycle


•Configuration & Performance Data

•Site Database

•BSIC Tuning

Preparation

•Network Freeze

•MR & HO Data Collection

Traffic Recordings •Data Processing

•Model Creation & Evaluation

IM Generation

•FP Generation

•Neighbor Plan Generation

FP Iterations •FP & Neighbor Plan Download

•Overshooting/Swap Cell Correction

•Physical Optimization

Implementation

Traffic & Coverage Heat

Maps

Traffic & Coverage Heat Maps

• MaxCell’s Optimization & Analytics Modules are also tailor-made for Spectrum Re-farming related Activities – Capable of Generation of Traffic & Coverage Heat Maps based on Traffic/Trace Recordings

captured on OEMs OSS e.g. Ericsson MRR & NCS Recordings – Sophisticated Proprietary Algorithms for Generating Traffic & Coverage Heat Maps on GIS Tool – Easy Visualization – Accurately Locates Coverage Gaps – Ability to Generate Coverage Heat Maps for Various Re-farming Scenarios – Aids in Visualization of High Traffic Carrying Areas

• Use Cases – Identification of Poor Coverage Areas for Underlay/Overlay/BCCH 900/BCCH 1800 – Depiction of Coverage in different Spectrum Re-farming Scenarios – New Coverage Sites Planning – Placement of Sites on GIS-Map


Coverage Heat Map Analysis

Heat Map – Existing 900 Band Underlay

Heat Map – Re-farmed Conversion of 900 Band Underlay to 1800 Band Underlay


Spectrum Refarming


Spectrum Refarming- Challenges & Task

Complex Network Transformation

RF Redesign – Coverage & Capacity

Multiple AFP Implementations

Traffic Balancing Between Layers

Cell Database Parameters Tuning

Spectral Efficiency

User Perceived Quality Improvement

Quality

• Spectral Efficiency & Frequency Plan Approach

• Activation of QOS Enhancement Features

• Cell Database Parameters Tuning

Capacity

• Traffic Dimensioning on New Spectrum

• Offloading Traffic towards Different Technologies

• TRX Dimensioning

Coverage

Identify Coverage Deficient Areas & Recommendations

Spectrum Re-farming Steps

• BSS Configuration Audit & Scenarios Identification – Analysis of Existing Radio Network Configuration – Identification of Different Spectrum Re-farming Scenarios

• Site Configurations – Underlay/Overlay; Band Segregation between BCCH/TCH • TRX Dimensioning • AMR HR Usage Thresholds • Traffic Sharing with LTE/UMTS • Traffic Handling Capacity / Utilization

• Measurement Reports Recordings – MR Data Collection for 2-3 Days on Existing Network – Creation for Project for Various Scenarios

• Running Simulations – KPI Performance Prediction for different Scenarios – Coverage & Capacity Sites Identification using Heat Maps – Identification of Optimum Re-farming Strategy/Scenario – Identification of BoQ

• Spectrum Re-farming – AFP – Neighbor List

• Cell Database Parameter Optimization – Frequency plan Fine-tuning – Parametric Optimization


Case Studies

Case Study - Parametric Optimization (UMTS)

• Pilot Pollution & Soft Handover Gains

• Antenna Configuration Optimization

• CPICH Power & Cell Individual Offset

• Cell & Per Neighbor Level Parameters Tuning

• RACH Optimization

• Cell Capacity

• Inter System iRAT & Inter Freq - qRxlevMin

• Soft Handovers Parameters Optimization

• Cell Coverage Shaping – Cell Individual Offsets

• Per Neighbor Relation Thresholds Setting

• reportingRange, TimeToTrigger, qOffsetxsn

• HSDPA

• Power Optimization / PDSCH Codes

Performance Optimization Areas

• Resource Utilization

• Maximization & Optimum Usage of RF Network Resources

• CAPEX & OPEX Reduction

• Key Performance Indicators

• Significant Performance Gains in following KPIs:

• RRC & RAB Setup Success Rate for CS & HSDPA

• Soft Handover Success Rate

• RAB Drop Call Rate for CS & HSDPA

• BLER Performance

• HSDPA Throughput

• Customer Experience

• Marked Improvement in KQIs

Gains

• Based on Live Network Performance Statistics & Traffic Recordings • Modeling the Performance at different Levels

• Per Cell • Multi Carrier Level • Traffic Level

• Per Neighbor Relation • Performance Data Capture Duration

• Performance Raw Statistics – 7 to 14 Days • Traffic / Trace Recordings – 3 to 5 Days

Core Optimization Concept

0

0.5

1

1.5

2

2.5

RRCSetupFailureRateCS

RRCSetupFailureRatePS

RABDroppedCallRateCS

RABDroppedCallRatePS

Pre

Post

Leading Indian Operator - Overall UMTS Pre – Post NBH KPIs (5 Working Days Average)

Significant Improvement is observed in all five KPIs

1840

1860

1880

1900

1920

1940

1960

1980

2000

2020

Pre Post

HSThroughputKbps


Case Study 1 - Parametric Optimization (GSM)

Leading Indian Operator - Overall GSM Pre – Post NBH KPIs

(5 Working Days Average)

The overall improvement in TCH Drop is 33%. Downlink Quality performance has also Improved and Bad Quality Samples reduced by 17%

96.50

97.00

97.50

98.00

98.50

99.00

99.50

CallComple on

Rate

HOSR GoodDLRxqual%

GoodULRxqual%

98.95

97.4497.29

97.71

99.30

97.80 97.75 97.81

Pre

Post

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

AverageofTCHDrop

AverageofSDDrop

1.051.11

0.70

1.07

Pre

Post

• Frequency Fine Tuning

• Optimization of ARFCN Assignment on BCCH & TCH

Layers on 10% of the Cells

• Cell & Per Neighbor Level Parameters Tuning

• Dual Band Layers – Traffic Balancing

• Handover Performance

• Handover Trigger Thresholds

• Cell Coverage Shaping – Tuning Offsets

• Per Neighbor Relation Thresholds Setting

• QoS Enhancement Features

• Activation of Features e.g. PS DL Power Control

• Repeated SAACH/FAACH

Performance Optimization Areas

• Resource Utilization

• Maximization & Optimum Usage of RF Network Resources

• CAPEX & OPEX Reduction

• Key Performance Indicators

• Significant Performance Gains in following KPIs:

• Drop Call Rate

• Handover Success Rate

• TCH Assignment Success Rate

• Good Voice Quality Samples

• Customer Experience

• Marked Improvement in KQIs

Gains

• Based on Live Network Performance Statistics & Traffic Recordings • Modeling the Performance at different Levels

• Per Cell • Dual Band Layers • BCCH; TCH 900 & TCH 1800

• Per Neighbor Relation • Performance Data Capture Duration

• Performance Raw Statistics – 7 to 14 Days • Traffic / Trace Recordings – 3 to 5 Days

Core Optimization Concept

Summary - QoS NBH


Summary - QoS NBH & BBH

Project Delivered for a Tier-1 Operator in Mumbai, India

Network Size- 4000+ Sites (Ericsson) Site Configuration- 900M & 900+1800M 3 Cycles of AFP and Automatic Parameter

Optimization Using MaxCell were Performed Significant Improvement Achieved in all

Major KPIs, NQI and Customer Complaints Project Highlights

28% Improvement in DLQ 21% Improvement in DCR 37% Improvement in Cells Meeting All

KPIs

Defaulter Cells in BBH


Summary- NQI & Customer Complaints

Project Highlights

Significant Improvement in Voice & Data NQI

142% Jump in Voice NQI 37% Improvement in Cells Meeting

All KPIs 42% Jump in Overall NQI 54% Reduction in Customer

Complaints Due to Radio Issues

Case Study - AFP

Summary- QoS

Project Delivered for a Tier-1 Operator in

Kolkata, India Network Size- 2700+ Sites (ZTE+NOKIA) Site Configuration- 900M, 1800M &

900+1800M Significant Improvement Achieved in all

Major KPIs Project Highlights

75%+ Defaulting Cells Shown Improvement- DLQ and ULQ

35%+ Reduction in Defaulters DLQ 28%+ Reduction in Defaulters ULQ

Defaulter Cells in BBH

Case Study - AFP


Significant improvement in Pre AFP DLQ Defaulter Cells More than 80% defaulter Cells have shown improvement Post AFP

More than 35% reduction in defaulter Cells count Post AFP

Improvement in defaulter cells in DL RxQual


Improvement in defaulter cells in UL RxQual

Significant improvement in Pre AFP ULQ Defaulter Cells More than 79% defaulter Cells have shown improvement Post AFP

More than 28% reduction in defaulter Cells count Post AFP

Case Study - AFP

40

Case Study – Spectrum Refarming

Summary- QoS Project Delivered for a Tier-1 Operator in

Mumbai, India Refarming- 5MHz Spectrum Carved from

1800MHz Network Size- 4000+ Sites (Ericsson) Site Configuration- 900M & 900+1800M Even after Spectrum Reduction Major KPIs

Were Restored to Previous Level. In Few KPIs Improvement was Achieved

Project Highlights 5MHz Spectrum Carved from GSM for

LTE Deployment Without Impacting GSM Network’s Existing QoS

Significant Improvement Achieved in SD Drop Rate- 12%

Fracton Technologies


Authorized Partner

Engineering

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