fracton tarec in offerings intro
TRANSCRIPT
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
3
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!
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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
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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
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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
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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
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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
• Configuration Dump – OSS XML Dump taken through OSS
• 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
• Huawei LTE Call Trace / PCHR Recording – Layer 3 Cell Trace Recordings
• 3 Days & Hourly recordings from 11:00 am to 12:00 pm
Optimization Strategy: Exploiting Performance Counters & Traces Recordings for
Assessing Traffic & Inter Cell behavior which forms the basis for Optimization Algorithms.
MaxCell 4G Solution for ZTE 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
• NetMax MR/CDT Recording – MR Recordings
• 3 Days & Hourly recordings from 11:00 am to 12:00 pm
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
• 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
• Megamon-Emil Recording – Layer 3 Cell Trace Recordings
• 3 Days & Hourly recordings from 11:00 am to 12:00 pm
Optimization Strategy: Exploiting Performance Counters & Megamon Recordings for
Assessing Traffic behavior which forms the basis for Optimization Algorithms.
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MaxCell Solution is Based on Iterative Parameters Tuning Cycles
MaxCell-3G Optimization Cycle
MaxCell Inputs
Optimization Targets
Optimization Engine
Parameters Implementation
Performance Assessment
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
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Typical Project Schedule – Improvements Observed in a Fortnight
MaxCell-3G Optimization Timelines
Week 1
•Selection of Optimization Area
•Existing Performance & KPI Assessment
•Define Optimization Targets – Voice Vs HSDPA
•Schedule Traffic Recordings – MR/Traces/Traffic Recording
Week 2
•Import Network Configuration
•Import CM, PM & Traffic Recordings Data
•Run MaxCell Optimizer
•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
•Iteration 2 (Fine Tuning): Recommendations Generation & Implementation
Week 5
•Pre Post Performance Assessment
•Benchmarking
•Project Report Submission
MaxCell 3G Solution for Ericsson Data Input Requirements
• Configuration Dump – OSS XML Dump taken through OSS
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
Assessing Traffic & Inter Cell behavior which forms the basis for Optimization Algorithms.
MaxCell 3G Solution for Huawei Data Input Requirements
• Configuration Dump
– OSS XML Dump taken through OSS
• 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
• Huawei CHR / PCHR Recording – Layer 3 Cell Trace Recordings
• 3 Days & Hourly recordings from 11:00 am to 12:00 pm
Optimization Strategy: Exploiting Performance Counters & Trace Recordings for
Assessing Traffic & Inter Cell behavior which forms the basis for Optimization Algorithms.
MaxCell 3G Solution for ZTE Data Input Requirements
• Configuration Dump
– OSS XML Dump taken through OSS
• 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
• ZTE Call Trace System (CTS) – Layer 3 Cell Trace Recordings
• 3 Days & Hourly recordings from 11:00 am to 12:00 pm
Optimization Strategy: Exploiting Performance Counters & Trace Recordings for
Assessing Traffic & Inter Cell behavior which forms the basis for Optimization Algorithms.
MaxCell 3G Solution for Nokia 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
• Megamon-Emil /CHR / MR Recordings – Layer 3 Cell Trace Recordings
• 3 Days & Hourly recordings from 11:00 am to 12:00 pm
Optimization Strategy: Exploiting Performance Counters & Megamon Recordings for
Assessing Traffic & Inter Cell behavior which forms the basis for Optimization Algorithms.
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MaxCell-2G Optimization Cycle
MaxCell Solution is Based on Iterative Parameters Tuning Cycles
MaxCell Inputs
Optimization Targets
Optimization Engine
Parameters Implementation
Performance Assessment
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
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Typical Project Schedule – Improvements Observed in a Fortnight
MaxCell-2G Optimization Timelines
Week 1
•Selection of Optimization Area
•Existing Performance & KPI Assessment
•Define Optimization Targets – DCR, Voice Quality & HOSR
•Schedule Traffic Recordings – MR / Traffic Recording
Week 2
•Import Network Configuration
•Import CM, PM & Traffic Recordings Data
•Run MaxCell Optimizer
•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
•Iteration 2 (Fine Tuning): Recommendations Generation & Implementation
Week 5
•Pre Post Performance Assessment
•Benchmarking
•Project Report Submission
Automatic Frequency Planning
AFP - Introduction
Strictly Confidential 24
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
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•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
Strictly Confidential 27
Coverage Heat Map Analysis
Heat Map – Existing 900 Band Underlay
Heat Map – Re-farmed Conversion of 900 Band Underlay to 1800 Band Underlay
Strictly Confidential 28
Spectrum Refarming
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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
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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
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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
Case Study 2 - Parametric Optimization (GSM)
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
Case Study 2 - Parametric Optimization (GSM)
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
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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
Strictly Confidential 39
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
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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
Strictly Confidential 41
Authorized Partner