LTE Planning And
Optimization
SVU Session April 2013
1
// Change has become endemic in the
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one of the most dramatic. // Social
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Efficiently Evolved Networks
Market Trends
Network cost
(current technologies)
Today’s challenges for operators:
Flat tariffs, increased operational cost, reduced ARPU
Changes are required to restore profitability
Revenue
Margin
€€
Network cost
(LTE)
• Evolve from voice-dominated to data-dominated networks
voice-dominated
• Fully utilize hardware and software features to maximize
capacity and bandwidth
• Automate the existing processes and improve the network’s
operational efficiency
data-dominated
time
Increase of data traffic requires additional
bandwidth
ARPU does not increase with data traffic
• Investments in 4G technology needs to be carefully
managed
• Enhance end user experience to increase revenue
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New applications require data-optimized access,
transport, and core.
Customer Challenges
Customers face the following Key Challenges when deploying LTE
• Effectively selecting and planning the sites to be used for LTE and their rollout
– 1 to 1 deployment of new sites at existing sites not necessarily best option
• Effectively Code planning
– Key ingredient to interference management, particularly significant at national/network
borders
• Effectively planning handover and traffic between layers in the multi-technology
environment
– Consideration needs to be given to available bandwidths per technology and subscriber
‘profile’
• Effectively minimizing interference both ‘intra-layer’ and ‘inter-layer’
– Whilst all technologies are ‘interference averse’. LTE performance is particularly prone to
interference-related degradation
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NSN NPO solution to address customer
challenges
NSN naturally offers ‘traditional’ LTE planning and optimization services to
customers
• Network design using the ‘usual suspect’ planning tools – ATOLL, ASSET , etc
• Drive test-based optimization and cluster acceptance services
• SON-based optimization
Additionally however, NSN offers the following differentiation:•
•
•
•
•
•
Automated Cell Planning with unique algorithms
Automated PCI planning using Modulo 3 based planning
Automated, patented interference identification and analysis (ICR)
Co-sited antenna 3D analysis and simulation
Automated neighbor generation and consistency checking
Comprehensive drive test analysis ‘in-house’ tools
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Customer Benefits
New
Maximise the utilisation of planned infrastructure capacity by high degree of
planning accuracy including specific LTE challenges such as high quality
PCI planning and interference avoidance
Linkage between LTE planning and optimization to provide closed loop
approach to solving detected network performance/interference issues
Focused deployment of capacity and coverage to match subscriber demand
Highly accurate optimization – real-life based simulation is employed to
validate changes to network before changes are deployed in network
Effective introduction of new technology that is ‘seamless’ to the network
users – auto neighbour generation and checking to aid smooth movement
of traffic between technologies/layers
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LTE Planning
6
// Change has become endemic in the
communications industry, with the rise
of social networking being perhaps
one of the most dramatic. // Social
media now has more than 500 million
users worldwide but was only founded
For
use total number of social
ininternal
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users in 2007. In the U.S., Facebook
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Radio Planning Process Overview
•
DIMENSIONING: Computation of number of sites to serve certain area to fulfil
customer requirements (Dim Tool)
•
NOMINAL PLANNING: Creation of a nominal Plan
– Coverage planning with planning tool (i.e. ATOLL, NetAct Planner, others)
– Detailed statistics
– ACP with Site selection (ACORN)
•
DETAILED PLANNING & Roll-out Support:
– Capacity analysis with planning tool
– Site validation & re-engineering
– BTS Parameter planning (i.e. frequency, paging groups, site data built with
default parameters)
– Databuild (LTE only or in addition to swap/Modernization/Multiradio)
•
PRE-LAUNCH OPTIMISATION: Cluster acceptance
– Drive test measurements, analysis and changes implementation
– Data build assessment/ consistency
– Performance monitoring
DIMENSIONING
Nominal Planning
Detailed Planning
Pre-launch Optimisation
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LTE Radio Planning Aspects
LTE Dimensioning Scope
• To address dimensioning of the air interface considering following
main aspects:
 Number of subscribers
 Service types
 Traffic generated by users
 Environment and clutter type (e.g. macro urban)
 Area/clutter size
•
•
To provide with respect to user assumptions the following outputs:
 Site count and site densities per area type
 Cell ranges and cell areas
 Sector and site throughputs
 Phase planning
 Calculations for multiple areas/clutters
 Input for access planning
To obtain balance between coverage and capacity requirements
LTE Coverage Dimensioning – Cell range
• Cell range estimation is similar to any other radio technology
•
Main parameters:

Frequency

Antenna height

Coverage probability requirement
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 Propagation model
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LTE Network Planning Considerations
•
There is certain trade-off between coverage and capacity
•
Tight frequency re-use 1 impacts strongly on SINR distribution
•
LTE planning is not feasible to do based on propagation only
•
Tools have to consider network load to get realistic UL and DL SINR,
throughput and coverage
•
Planning is similar to Mobile WIMAX and HSPA
LTE Capacity Dimensioning
Maximum user throughput
 Depends on RF features (e.g. MIMO) and bandwidth.
 Doesn’t depend on radio planning
• Average user throughput
 Typically defined as single user throughput (no other users in the
cell at the same time).

Typical measure for drive testing (cluster acceptance)
• Average cell throughput
 Used in capacity dimensioning
 Typically monitored with network KPIs
 Reliable results require considerable amount of users
•
Required cell edge throughput
 Coverage requirement in dimensioning
NSN LTE Planning
Innovations
9
// Change has become endemic in the
communications industry, with the rise
of social networking being perhaps
one of the most dramatic. // Social
media now has more than 500 million
users worldwide but was only founded
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users in 2007. In the U.S., Facebook
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The Main Players
NSN has two major tool platforms
that provide major differentiation in
LTE Planning
ACORN - NSN’s own Automatic Cell
Planning tool
• Automated generation of quality- and costoptimized nominal cell plans
• Coverage, capacity and cost-driven
automated site selection
• Multi-technology automated network
consolidation
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MUSA - A multi-module analysis and
optimization platform that supports a wide
range of activities
• 3G, 2G, LTE (and other technologies)
• Drive test post-processing (but can
integrate data from other sources, e.g.
PMC)
• NSN networks and Multivendor
technologies
• Coverage Analysis and Optimization –
Irregular Coverage recognition
• Neighbor Analysis and Processing
• Radio Parameter Adjustment
• CSFB Analysis
ACORN Automated
Cell Planning
11
// Change has become endemic in the
communications industry, with the rise
of social networking being perhaps
one of the most dramatic. // Social
media now has more than 500 million
users worldwide but was only founded
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ininternal
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Automatic Cell Planner Utilization
As an alternative to manual, traditional radio planning performed using NetAct
planner (or equivalent) ACORN can be used instead for LTE initial design.
• The advantages of using ACORN are :– The ability to load a list of potential LTE sites together with the coverage and capacity desired
targets
– Based on the above, the tool then automatically selects and optimises site configuration
 Uses advanced modified planning algorithms to provide an optimized network plan based on the input
parameters and targets
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Presentation
ACORN and LTE Site Selection Use case
ACORN can be used to assess site selection on the following basis
• Assume Site Rollout based existing network (exemplary hexagonal project)
– Inputs
 Rough traffic expectations
 Site-to-site distance
• Subsequently generate pathloss predictions for the newly created LTE sites
– Pathloss predictor method generates predictions for antenna location according to selected
pathloss model
• Based on the above run ACORN’s “LTE Site Selection Algorithm” method
– Site Selection Algorithm selects best suited sites for the network according to coverage restrictions
– Utilizes modification to Shannon capacity bound, considers Bandwidth Efficicency factor, SNR
efficiency factor and available bandwidth to accurately derive expected coverage and capacity for
planned cells
• Subsequently verify the output of ACORN using its “LTE Site Selection Reports”
functionality
–
Site Selection Reports analyses a design produced by Site Selection Algorithms method
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Importance of getting the site count ‘right’
Many operators are tempted to place LTE sites on a one-to-one basis with
existing 3G sites………..
But we have a Key message:
The number of LTE cells when converted from all existing 3G sites is often
seen to be more than necessary to support initial traffic densities are
coverage requirements, and cell overlapping and hence inter-cell
interference can be excessive in the outdoor environment.
Careful planning and cell/antenna selection process, and initial RF tuning is
important to the LTE field performance
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Example - Improving performance by blocking
excess cells
All cells, before optimization
Blocked cells, after optimization
FT_04.1 Mobility DT DL - Throughput comparison
100%
90%
80%
70%
CDF %
60%
All cells
Blocked cells
50%
40%
30%
Ave throughput improved from
23.34Mbps to 26.78Mbps, i.e. 14.7%
20%
10%
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Presentation / Author
Phy DL/ Date
tput
(Mbps)
MUSA - LTE PCI
Planning
16
// Change has become endemic in the
communications industry, with the rise
of social networking being perhaps
one of the most dramatic. // Social
media now has more than 500 million
users worldwide but was only founded
For
use total number of social
ininternal
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users in 2007. In the U.S., Facebook
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LTE PCI Planning - Overview
While in LTE several planning aspects are easier than in the past, a crucial
activity for the quality of the network is still LTE Code planning.
NSN has developed a LTE Code planning methodology based on the use of
a module in the MUSA tool
• Allows PCI planning utilizing modulo 3 - Why is this important?
– As will be described in a moment – the modulo 3 value relates to the PSS sequence at the cell
– PSS sequence is used by UE as part of identifying the cell that they should be ‘communicating’
with
Extensive tests done with respect to other commercial tools (Asset, Atoll)
clearly show from simulations and in-field results a higher network quality
from the use of NSN LTE Code planning techniques
• Analysis performed based on an area of a tier #1 customer network showed MUSA plan
has “Zero” intra eNB PCI clashes and less direct collision with inter-eNB neighbors
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Presentation / Author / Date
LTE PCI Planning - What is the PCI?
•
•
Physical Layer Cell Identity (PCI) identifies a cell within a network
There are 504 Physical Layer Cell Identities -> PCI is not unique!
Physical Layer Cell Identity = (3 × NID1) + NID2
NID1: Physical Layer Cell Identity group. Defines SSS sequence. Range 0 to 167
NID2: Identity within the group. Defines PSS sequence. Range 0 to 2
• PCI is not the E-UTRAN Cell Identifier (ECI)
• ECI is unique within a network
• ECI does not need to be planned. ECI value is set by the system
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PCI Planning - Modulo3 Rule
Rule:
• Avoid assigning to the cells of one eNB PCIs with the same modulo 3
Reason:
• PSS defines NID2. There are 3 NID2 in a group so PSS is generated using 1 of 3 different sequences
• If two cells of the same eNB have the same mod3(PCI) it means they have the same NID2 (i.e. 0, 1 or
2) and the same PSS sequence
– PSS is used in cell search and synchronization procedures: Different PSS sequences facilitate
cell search and synch procedures
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Introduction: Conventional Physical Cell ID Planning
Illustration of Conventional PCI planning (3-sector-site and synchronized neighbors)
Serving Cell Signal
Different PCI mod 3: No collision of
neighbor CRS/PSS
Same PCI mod 3: Collision of
neighbor CRS/PSS.
PCI mod 3 = 1
PCI = 4
PCI = 5
PCI = 2
PCI mod 3 = 0
Same PCI: Signal is too weak
to be detected
PCI = 0
PCI mod 3 = 2
PCI = 1
PCI = 2
Conventional Approach:
Avoid neighbor cells which have the same PCI
•
•
•
Neighbors with same PCI mod 3: CRS and PSS detection is poor because of their collisions with each other.
Under conventional PCI planning, collisions are not optimally avoided, especially under omni-site case.
Conventional PCI planning results in poor KPIs especially at the cell-edge of synchronized LTE
networks
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Impact of PCImod3 collision on tput, TD-LTE
•
•
Case: UE at the border of two cells who have the same PCImod3, RSRP from both cells = 67dBm in both measurement cases (only PCI changed)
NSN 7210 TD dongle, 2.6GHz, 10MHz bandwidth
16
14
tput, Mbps
12
10
no PCImod3 collision
8
PCImod3 collision
6
4
2
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
seconds
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Neighbor Consistency
Checking
Using MUSA tool ADA module
22
// Change has become endemic in the
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media now has more than 500 million
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ADA – Ancillary Neighbor Analysis
• ADA is a dedicated module of MUSA tool,
specifically design for neighbors analysis.
• ADA module can work with NSN database or it
can also import data from Excel sheets.
• ADA can display relations, highlighting different
cell set (3G, 2G, Layer1, Layer2, clusters, etc.)
and neighbors, using raster, vectorial or web
maps (e.g. OpenStreetMaps).
• Several operations can be applied to neighbor
lists and cell sets (union, intersection,
difference, filtering, sorting, etc.)
• Powerful neighbor list check are supported:
missing neighbors (cell and site missing),
co-location checks, SIB#11 and maximum
count checks, etc.
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MUSA – Co-site
Antenna modeling
24
// Change has become endemic in the
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media now has more than 500 million
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Co-siting: Achieving sufficient isolation
Achieving the isolation requirements depends upon
the antenna sub-system design
Interference
• dedicated feeders and antenna
• dedicated feeders and shared antenna
• shared feeders and antenna
If sites belong to different operators then it is likely
that dedicated feeders and antenna are used
Isolation is achieved by ensuring there is sufficient
isolation from:
•
•
•
•
antenna positioning
feeder loss
combiner isolation
antenna isolation
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Presentation / Author / Date
Feeders
Feeders
MUSA Antenna Co-site Module
Antenna Co-site tool can calculate the actual decoupling
between each antenna involved after input of exact
location of antennae and further details (distance between
antennae, azimuths, patterns etc.) and highlight the most
critical decoupling
CoSite Tool uses a patented method to rebuild the 3D
antenna pattern from H and V plane diagrams.
In case of issues RF planner can play with antenna type,
positioning, height, azimuth and tilt in order to reach the
needed
decoupling
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LTE Optimization
27
// Change has become endemic in the
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media now has more than 500 million
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LTE Optimization Key areas
Coverage/Physical Layer Optimization
•
•
•
•
Interference identification and minimization
Site configuration issue identification and resolution – e.g. crossed MIMO antennas
Overshooting cell detection and resolution
Network Coverage Certification
Performance Optimization
•
•
Call set-up Optimization
– PRACH phase
– Post-PRACH phase
Call drop Analysis
– UE-initiated and eNB-initiated dropped calls
– Neighbor management & Handover issues
– Peak Throughput Optimization
LTE Inter-frequency Optimization
•
Impacts of system bandwidth variations between frequencies
CSFP Optimization
•
•
Call set-up time
‘Return to LTE’ Delay
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Importance of Physical Layer Optimization
Basic physical RF optimization is very important
• Clear cell dominance areas, minimize cell overlapping
• Avoid sites shooting over large areas with other cells
• “Can’t fix bad RF by tuning parameters” – except neighbor definitions
Antenna tilting and antenna placement has big impact on other cell interference
Building good dominance is essential for network performance
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Example Performance Improvement
• Massive improvement in the
performance by basic physical layer
optimization in a cluster
Cluster
level
drive test
results
Diff.
Drive Test Result
(Antenna Tilting)
Unit
DL Throughput
(Mbps)
5 Mbps ↑
Handover Attempts
(#)
33 % ↓
Average SINR
(dB)
2.3 dB ↑
Experienced
improvement
Average CQI
0.5 ↑
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MUSA - Irregular
Coverage Recognition
Finding the Interference
31
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MUSA - Irregular Coverage Recognition
LTE, like UMTS, is a system that normally utilizes cells on the same frequency
band to provide network coverage and capacity
• In order to work effectively these types of networks have to be accurately planned for a cell
coverage perspective as any ‘unwanted’ coverage from a cell will have undesirable impacts
on network performance
• Ideally cells should only ‘see’ each other at the defined handover border areas
• This equates to a strong need to minimize interference in the network
ICR is a unique and patented methodology using a MUSA module which
analyzes drive test (or geo-located measurements on 3G) to detect
interfering and interfered cells
• These cells are then ranked in order of network impact to provide a structured approach to
addressing interference issues in the network
• Another module of the tool provides simulation of the effects of changes in the RF
conditions based on antenna and power changes with regard to levels of interference
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Presentation / Author / Date
Irregular Coverage – What do we mean?
Here we see that there are ‘islands’ of unwanted or ‘irregular’
coverage in the Cell ‘A’ coverage area are being produced by
Cell ‘B’
Objective of ICR is to capture these areas and to optimize Cell
‘B’ coverage to remove them
Cell ‘B
Cell ‘A
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Coverage Optimization - ICR Methodology
In this example 3 cells and their interaction are considered
• The total number of islands will be calculated for all the cells;
based on the “island” report it is possible to rank the cells on the
base of the coverage fragmentation and apply the appropriate
optimization changes.
• Two “filters” are then applied to the “island” aggregation rules:
• On Surface = x (x= min number of pixel to be an island)
• On Distance = y (y=max distance between pixels to separate
islands)
• Tool also island weighted on surface and distance
Applied Filter D=1 S=1
Cell A: 4 islands
Cell B: 5 islands
Cell C: 4 islands
Applied Filter D = 1, S = 2
Cell A: 4 island
Cell B: 2 island
Cell C: 2 island
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Applied Filter D = 2, S = 2
Cell A: 1 island
Cell B: 1 island
Cell C: 1 island
B,C = interfering cells
A = Interfered cell
Effects of Filtering
Applied Filter D=1 S=1
Cell A: 4 islands
Cell B: 5 islands
Cell C: 4 islands
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Applied Filter D = 1, S = 2
Cell A: 4 island
Cell B: 2 island
Cell C: 2 island
B,C = interfering cells
A = Interfered cell
Applied Filter D = 2, S = 2
Cell A: 1 island
Cell B: 1 island
Cell C: 1 island
ICR- Results
Critical Cells
6641F1_3
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22062F1_3
23419F1_2
77751F1_3
77751F1_1
47117F1_2
748F1_2
23400F1_3
77755F1_1
3221F1_3
37381F1_3
10659F1_3
36892F1_2
ICR - Remediation
MUSA is able to simulate the
impact of antenna changes
(tilt, azimuth, type, height) on
the collected measurements
• The changes are driven by ICR
and help the planner in having a
wide understanding of the
potential impact of RF changes
• It is also possible to re-calculate
ICR after the changes in order to
verify the correctness of proposed
changes
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ICR– Real example
Coverage ‘splash’ can
clearly be seen on A562
Bridge
Coverage splash remediated
using ICR detection and analysis
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CSFB and VoLTE
Radio Optimization
39
// Change has become endemic in the
communications industry, with the rise
of social networking being perhaps
one of the most dramatic. // Social
media now has more than 500 million
users worldwide but was only founded
For
use total number of social
ininternal
2005. The
Unique
document
identifier
(ID) / //Version
networking
users
exceeded
Email number / Life cycle status
users in 2007. In the U.S., Facebook
© Nokia Siemens Networks 2013
overtook social mediain time spent on
Radio CSFB Performance Optimization
Scope/Methodology:
Test cases:
 CSFB feature optimization by drive test
 Redirect wo SIB 3G/2G; Redirect w SIB 3G/2G; MOC/MTC; Drive/Pedestrian; Multiple
KPIs:
radio conditions
 Anite Nemo LTE; Anite Nemo Analyze / MUSA
 CSFB Success Rate; Incomplete procedure; Setup Failures w Reject; Setup Failures wo
Reject; CSFB in Connected State; CSFB w Alerting; Mean Setup Time; Mean Setup Time
components by protocol; Mean Return Time;
Customer Benefits
 CSFB strategy (target band, SIB) and feature configuration optimization
Resources needed:






Tools:
Resources Cost:
Timeline:
Tools Cost:
Travel Cost:
Other Cost:
Radio Eng
3 MWD to drive test; 7 MWD for analysis and recommendation
Upon resource availability
Expecting to use local tools
Upon resource availability
No
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CSFB Testing & Optimization - MUSA
CS Fallback is today the most used method to perform CS calls when connected to an LTE
network
• CSFB need to be carefully tested and optimized especially from the call setup time point of view
NSN has created a MUSA functionality called to analyze overall CSFB performance and time
spent in the different phases of CSFB
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Presentation / Author / Date
Radio VoLTE mobility optimization
KPIs:




Customer Benefits
 Configuration optimization for mobility in VoLTE
Resources needed:






Scope/Methodology:
Test cases:
Tools:
Resources Cost:
Timeline:
Tools Cost:
Travel Cost:
Other Cost:
Intra-system and inter-system mobility optimization by drive test
MOC/MTC; Drive/Pedestrian; Multiple radio conditions
Anite Nemo LTE; Anite Nemo Analyze / MUSA
Call Drop; Intra-LTE HO Success Rate, LTE to UMTS Inter-system HO Success Rate;
LTE to GSM Inter-system HO Success Rate; UMTS to LTE Inter-system HO Success
Rate; GSM to UMTS Inter-system HO Success Rate;
Radio Eng
3 MWD to drive test; 7 MWD for analysis and recommendation
Upon resource availability
Expecting to use local tools
Upon resource availability
No
For internal use
Unique document identifier (ID) / Version number / Life cycle status
42
© Nokia Siemens Networks 2013
SmartLabs-assisted VoLTE Analysis
SCOPE
Smart Lab Scope
Project scope is to perform LTE VOLTE performance analysis for selected customer devices in
NSN Smart Lab LTE network with the focus on analysis of device performance in terms of
network impact, user experience and battery consumption.
Tests are executed with key devices on agreed test cases . Smart lab test network is configured
based on customer recommended configuration.
Test will be conducted in different RF environment:
• Good RF: SINR >20 dB; Bad RF: SINR<3 dB; Med RF: SINR ~ 11 dB
NPO Extended Scope
• NPO uses inputs from Smart Labs and perform field test to verify and fine tune the parameter
optimization.
• NPO compares Smart Lab and live network KPIs and generate insights, and focus on
optimizing VoLTE performance as well as end-user experience.
For internal use
Unique document identifier (ID) / Version number / Life cycle status
43
© Nokia Siemens Networks 2013
Confidential between TMO and Nokia Siemens Networks
SmartLabs-assisted VoLTE Analysis cont.
PROJECT DETAIL
Focused network settings & scenarios:
• Battery consumption: CDRX optimization for VOLTE
• VOLTE call setup time
• Voice quality & battery consumption:
•
•
•
•
plain WB-AMR
plain NB-AMR
WB-AMR with background FTP
NB-AMR with background FTP
• Delay/User experience: intra/inter eNB handover
• SRVC
MEASURED KPIs
•Network Impact KPIs:
•Data sessions
•Traffic volume
•Radio signaling load
•Core signaling load
•Quality of Experience KPIs:
• Speech quality (POLQA MOS)
• MO/MT Call setup time
• Battery Consumption
• Handover preparation duration
•Control Plane handover duration
•User Plane data interruption duration
•NPO additional KPIs:
•Capacity
Inputs from Smart Labs to NPO
• 8 KPIs
• Regular
information exchange between NPO and Smart Labs
For internal use
Unique document identifier (ID) / Version number / Life cycle status
44
© Nokia Siemens Networks 2013
Confidential between TMO and Nokia Siemens Networks
SmartLabs-assisted VoLTE Analysis
cont.
PROJECT OUTCOME
NSN Smart lab provides detailed results of the 8 measured KPIs for all tested devices and
scenarios.
NSN Smart Lab report suggests the optimized CDRX parameter setting for VOLTE.
NSN NPO report suggests ways to optimized VoLTE performance as well as end-user
experience by identifying bottlenecks in the E2E VoLTE network.
Customer uses Smart lab test results for reference.
.
For internal use
Unique document identifier (ID) / Version number / Life cycle status
45
© Nokia Siemens Networks 2013
Confidential between TMO and Nokia Siemens Networks
And finally………..
46
// Change has become endemic in the
communications industry, with the rise
of social networking being perhaps
one of the most dramatic. // Social
media now has more than 500 million
users worldwide but was only founded
For
use total number of social
ininternal
2005. The
Unique
document
identifier
(ID) / //Version
networking
users
exceeded
Email number / Life cycle status
users in 2007. In the U.S., Facebook
© Nokia Siemens Networks 2013
overtook social mediain time spent on
NSN key contacts , links to the relevant collaterals
Key Contacts:
Global Radio Stream PdM : Mark James
Tools: Claudio Mattiello
Solution Architecture Manager: Eric Kroon
Capability Development Manager: Raija Lilius
Collateral link to be added……………….
For internal use
Unique document identifier (ID) / Version number / Life cycle status
47
© Nokia Siemens Networks 2013