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White Paper for InfoVista
Optimisation automation:
Immediate gains for today, SON enabler for the future
May 2013
Dr Mark H Mortensen, Patrick Kelly and Anil Rao
.
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Analysys Mason Limited 2013 May 2013
Contents
1 Executive summary 1
2 State of the network planning and optimisation domains 1
3 Reducing CAPEX with integrated NPM and NP&O 2
4 Up-to-date network intelligence for Automated Network Optimization and Planning 3
5 Optimization automation is the right step towards realizing SON 5
6 The market is already moving forward 6
7 Recommendations: modernise, integrate, automate 7
About the authors 8
List of figures
Figure 3.1: Manual NP&O processes using out-of-date data lead to inaccurate resource allocation ............... 3
Figure 4.1: Optimisation automation by integrating NP&O and near real-time NPM systems ........................ 4
Figure 4.2: Capex savings ................................................................................................................................. 4
Figure 5.1: Benefits of SON ............................................................................................................................. 5
Figure 5.2: SON-enabled NP&O processes ...................................................................................................... 6
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Optimisation automation: Immediate gains for today, SON enabler for the future | 1
Analysys Mason Limited 2013 May 2013
1 Executive summary
Mobile communications service providers (CSPs) are under tremendous pressure to increase network capacity to
support the ever increasing mobile data consumption rates. It is therefore imperative that they take steps to
efficiently utilise the limited network resources. CSPs have traditionally used network planning software to add the
right capacity at the right place at the right time, in order to provide the best customer experience, without expensive
overbuilds. Doing this optimally requires high-quality, current data. In addition, there is also a goal of optimisation
of available resources modifying the existing network configuration, without adding resources, in order to optimise
the use of current network resources while providing the latest services at the highest possible quality levels.
Network planning is a complex exercise with a typical planning cycle often taking months. With new mobile
access technologies coming into use such as small-cell, HSPA/HSPA+ and Wi-Fi data offload, the planning
process is becoming much slower and time consuming, and without streamlining the existing planning processes,
CSPs will increasingly struggle to find the optimal solutions. Compounding this problem further is the use of in-
house tools and manual processes that use outdated network performance data for planning. This leads to poor
quality network plans, inaccurate allocation of network resources and stranded capex. Analysys Mason estimates
that mobile CSPs have at least 5% of their network capacity stranded because of this, plus inaccurate forecasting.
Innovations in network planning and optimisation (NP&O) software systems will address this problem to some
extent. However, integrating a network performance management (NPM) system with a modern, automated
NP&O system enables the CSP to use latest network performance data for network planning improving the
overall accuracy and quality of network plans and paves the way for optimisation automation. Models indicate
that optimisation automation can recover as much as 20%40% of the stranded capacity, providing improved
capital expenditure (capex) efficiency and, through the automation of manual processes, additional operational
expenditure (opex) savings.
Furthermore, optimisation automation is the best current step towards realising the potential of self-
optimising/organising networks (SON).
2 State of the network planning and optimisation domains
Two major recent advances in NP&O technologies have recently been introduced, although many CSPs have
not yet updated their operations, and software systems, to take advantage of them. These CSPs are suffering
higher than needed opex costs.
First is the introduction of true planning systems, rather than mere planning tools. These modern systems
provide much more than the tools by centralising the data, synchronising the interdependent, layered plans, and
ensuring consistency amongst the various technology and geographic plans. These systems can also radically
reduce the planning cycle times from four to six months to a matter of weeks.
Second is the availability of advanced performance information from the network equipment itself. With the
right performance management system, a mobile CSP can determine the accurate utilisation and actual
performance that customers perceive and plan and tune their networks accordingly. This is eliminating
expensive drive tests and improving the quality of the data as measured at the handset, which are, more often
than not, inside buildings.
So what can be gained by combining these two advances? That is the subject of the rest of this white paper.
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Analysys Mason Limited 2013 May 2013
3 Reducing CAPEX with integrated NPM and NP&O
Beyond planning the right location for capacity augmentations, CSPs now use advanced NP&O software tools
to optimise their networks. The aim of such optimisation is to modify the existing network configuration
parameters, without adding resources, in order to optimise the use of network resources while providing the
current services at the highest possible quality levels. Driven by such CSP requirements as well as innovation
through R&D, the vendors of NP&O software have developed features that tackle the increase in technology
complexity and optimise the planning exercise. Below are three such examples:
Advanced antenna modelling: with heterogeneous networks, it is important to have good antenna format
for all cells (macros, micros, etc.). Most radio planning tools have separate antenna files for different
electrical tilts, electrical azimuths, different frequency bands, etc. This can mean a huge number of antenna
files. This is not practical at all from a users perspective. In the case of antennas being shared by different
technologies or frequency bands, you will have multiple sectors that share the same antenna, it is critical
to have a single antenna file, to indicate that the physical parameters of the antenna are shared by all the
sectors that use that antenna, while electrical tilts can potentially be different for the sectors that share that
antenna.
Support for LTE-Advanced: innovative CSPs, especially in North America and in developed AsiaPacific
countries are either already in the midst of strategic planning for LTE-A or will soon start thinking about it.
LTE-A offers capabilities that are dedicated to HetNet, and that are meant to increase network capacity,
which will be critical to remain competitive and protect margins.
Efficient hotspot identification for planning and optimisation: some innovative capabilities allow
engineering and optimisation teams to focus on the planning and re-planning exercises that would provide
the best return on investments. For example this can be the ability to leverage social media information
(such as Twitter) as one of the data feed (crowdsourcing). The ability to leverage polygon files (including
building heights and building types) as well as the ability to define indoor-to-outdoor traffic ratios are also
important innovations.
On its own, advancements in the NP&O technology will continue to benefit the CSPs, enabling them to optimise
deployment costs. However, there is a significant opportunity to go one step further and achieve higher network
capex efficiency through additional innovations such as using up-to-date network performance data in the
NP&O processes and realising the benefits from the synergies achieved between NP&O and NPM.
Since many CSPs use traditional planning tools, the NP&O process takes many months to complete a planning
cycle since many geographies and technologies must be planned together and in addition, the planners use
different tools for each technology (3G, LTE, etc.) in some cases, in different departments (engineering,
optimisation, CTO office, etc.). Typically, in a mobile NP&O scenario, two inputs are needed to create the
traffic matrices needed to plan the RF, backhaul and core network capacity current network usage and
configuration, and new traffic forecast due to growth. The long planning cycle times, together with out-of-date
network and forecast information lead to suboptimal placement of resources by the planning process. This leads
to networks with as much as 5% of their capacity stranded and unused. Of course, growth of the network will
eventually use up the incorrectly placed capacity but meanwhile, it is unused. And other areas that received
insufficient resources perform poorly until augmented, leading to poor quality of service and customer churn.
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Optimisation automation: Immediate gains for today, SON enabler for the future | 3
Analysys Mason Limited 2013 May 2013
The situation is particularly unfortunate because many modern NPM systems are picking up near real-time data
from the network. But the long planning processes render this data obsolete by the time it is used. This is shown
in Figure 3.1 below, where NP&O processes are entirely manual, use poor quality historical performance
management data, and take months of planning before the network modifications are planned and implemented.
Figure 3.1: Manual
NP&O processes using
out-of-date data lead to
inaccurate resource
allocation [Source:
Analysys Mason, 2013]
4 Up-to-date network intelligence for Automated Network
Optimization and Planning Building highly performing
cost-efficient networks
Services deployed over an LTE network will require consistent uniform performance management across the
access and backhaul networks. The challenge in todays mobile networks is matching broadband demand with
available network capacity. Technologies such as LTE enable mobile operators to meet these demands via
greater spectral efficiency and targeting an LTE overlay strategy in areas where demand exceeds available
capacity.
Mobile operators can benefit from performance management systems because data can be used in near real-time
to make informed decisions on how to tune available network resources including such techniques as traffic
steering and optimisation of the radio access technology (RAT).
Collecting near real-time network performance data enables mobile CSPs to defer capital spending in parts of
the network where it is not yet needed and it improves the network planning and optimisation process. A good
quality plan puts the right resources at the right place at the right time, decreasing congestion, efficiently using
capital, and increasing customer satisfaction (see Figure 4.1).
Problems
Stranded capacity and capex Use of old and out-of-date
network performance data
Long planning cycle time Few what-if analyses
NP&O NPM
Up-to-date
network
performance
data
Mobile network(macro-cell, backhaul, IP-core)
Manual
optimisation
Data quality
GreatGood
Poor
TODAY: networks with stranded
capacity and capex
Network
modifications
take months
Jan
March
Feb
Manual feeds
of historical
data
Manual
congestion
control
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Optimisation automation: Immediate gains for today, SON enabler for the future | 4
Analysys Mason Limited 2013 May 2013
Figure 4.1: Optimisation
automation by
integrating NP&O and
near real-time NPM
systems [Source:
Analysys Mason, 2013]
Near real-time network performance management combined with network planning systems enables accurate
reporting for service coverage maps. The planning process can then do a just-in-time resource allocation in the
order of days for the RF, backhaul, and core networks. By automating the process of retrieving the information
from performance management systems and feeding it into NP&O systems, CSPs can achieve significant
benefits in the form of optimisation automation. This leads to accurate network resource allocation by pin-
pointing exact locations with network performance degradation based on existing traffic patterns, and provides a
basis for predicting the locations where future performance degradation is likely to occur. In addition, CSPs can
reduce human error by automating the feeds from NPM systems into NP&O systems.
Proactive coverage maps, automatically produced, can be used to tune the network plan to ensure continued
quality of service to key corporate and other high-value customers, as the traffic increases. This will be
particularly important as LTE high-speed data is introduced and its use grows with rising numbers of LTE-
enabled mobile devices and the ensuing increase in bandwidth usage. As integrated NPM and NP&O systems
are implemented with more, faster automated optimisation processes, benefits will increase as the systems are
able to optimise the network during anticipated diurnal variations in network traffic conditions. Analysys Mason
estimates that mobile CSPs can recoup 20% to 40% of the 5% stranded capex by implementing optimisation
automation processes (see Figure 4.2).
Figure 4.2: Capex
savings [Source:
Analysys Mason, 2013]
Benefits
Current up-to-date networkperformance data
Reduce stranded capacity
Reduce stranded capex Up-to-date coverage maps Network optimisation based
on high-value customers
NP&O NPM
Up-to-date
network
performance data
Mobile network(macro-cell, backhaul, IP-core)
Manual
optimisation
Data quality
GreatGood
Poor
TOMORROW: integrated NP&O
and NPM
Network
modifications in
days/weeks
M T W T F
Capex
(30%
of total
mobile
CSP
cost)
Up-to-
date
n/w perf.
data
Accurate
forecasts
Efficient
n/w
planning
5% stranded
capex
1%2% savings on
capex
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Analysys Mason Limited 2013 May 2013
5 Optimization automation is the right step towards
realizing SON
Mobile CSPs worldwide are in different stages of deploying LTE access technology. CSPs are also pursuing
strategies to enhance coverage using the least possible capex such as deploying 3G/4G small cells, femtocells
and Wi-Fi offloading capabilities. This will result in a heterogeneous network (HetNet) that will be significantly
more complex. Consequently, the HetNets will place significantly high demands on the network planning and
optimisation teams, systems and processes. Manual planning cycles will take even longer with a higher
probability of human error leading to inaccurate and misallocation of network resources. If the CSPs are not
careful, inefficient network planning and optimisation can result in increased costs and potentially, defeat the
whole purpose of a HetNet strategy.
Mobile CSPs and vendors have tried to tackle this problem in the past. In 2007, when the long-term evolution of
3GPP (LTE) was being tabled, all parties, including mobile CSPs and vendors, agreed the need to operate the
system at a significantly lower cost compared to UMTS. This requirement gave rise to features for networks to
self-organise and self-optimise (see Figure 5.1). The SON standards were defined with three architectural
options based on where the SON algorithms are deployed in the base station/controller (centralised), in a
central server connected to northbound SON interfaces (distributed) or in a two-stage control architecture with a
centralised SON algorithm controlling distributed algorithms over a dedicated SON interface (hybrid).
Figure 5.1: Benefits of
SON [Source: Analysys
Mason, 2013]
Equipment manufacturers began back-porting SON features to 3G technology in earnest in 2010, as CSPs began
to understand that to meet the rising demand for data, it could be more cost-effective for them to expand HSPA
and HSPA+ high-speed data capacity on the existing 3G infrastructure in many locations.
However, to realise the full potential of SON will require network-level functions in a hybrid architecture,
requiring that NPM and NP&O functions be integrated, automated and, eventually, put in control of the network
in a closed-loop cycle. CSPs will also require the tools and mechanisms to validate vendor-driven SON
implementations to gain full confidence in the optimisation process.
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Analysys Mason Limited 2013 May 2013
Analysys Mason believes that true SON capabilities, while still eight to ten years away from being a full reality,
will be incrementally implemented. One such incremental step towards realising SON is the optimisation
automation. With more small cells in the network, a richer network performance data will be available that can
make the planning and optimisation process even more efficient. Eventually, as SON features roll out in the
equipment, the NP&O system, using up-to-date performance network intelligence, will be able to optimise
further the network, backhaul, and core IP networks. In the short term, these will be implemented both as open-
loop systems, providing recommendations to the network engineers, who control the network configuration, or
closed-loop systems, where the NP&O system itself optimises the network in near real-time (see Figure 5.2).
Figure 5.2: SON-
enabled NP&O
processes [Source:
Analysys Mason, 2013]
6 The market is already moving forward
Over the last several years, vendors have been strongly positioning themselves to offer the centralised software
module of a hybrid SON. In line with this strategy, vendors are integrating their network performance
management capabilities with network planning and optimisation capabilities to provide mechanised, and
eventually automated, network optimisation for 3G/4G networks and future HetNets. To this end, a number of
traditional NPM independent software vendors and network equipment manufacturers have acquired NP&O
companies. TEOCO, a provider of fault and network performance management software, bought Schema for its
backhaul and automated cell planning capabilities. AIRCOM International, a provider of RF planning software,
built a network performance management solution and acquired configuration management through its
acquisition of Symena. Similarly, Ericsson bought Optimi, a network optimisation and management specialist;
Nokia Siemens Networks acquired IRIS Telecom; and Cisco bought Intucell, a SON software supplier.
In a move similar to these acquisitions, InfoVista acquired Mentum, a leading provider of network planning
systems, in November 2012. InfoVista has deployed its NPM solutions at a number of tier-1/tier-2 mobile CSPs
to normalize massive amount of network KPIs/KQIs, via its mobile packs. InfoVistas Mobile Knowledge Pack
is a product that monitors the mobile infrastructure of leading suppliers including Ericsson, Cisco, NSN, Alcatel
and Huawei to generate up-to-date reports on network performance and network quality of service in a multi-
vendor network. Incorporating the latest network performance data and forecast network intelligence into the RAN
and IP transport planning processes will enable InfoVista to automate network optimisation processes and offer the
accuracy and efficiency essential for optimised and just-in-time RAN and backhaul investments.
Benefits
Fast forward optimisation Opex benefit opportunity with
energy savings
Network performance improvement in real time
Automated what-if analysis features
NP&O NPM
SON Closed-loop optimisation
Real-time network
performance data
HetNet(macro-cell, micro-cell,
Wi-Fi, backhaul, IP-core)
SON Open-loop
optimisation
Network
modifications in
hours/minutes
Data quality
GreatGood
Poor
FUTURE: SON
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Analysys Mason Limited 2013 May 2013
7 Recommendations: modernise, integrate, automate
Mobile CSPs must consider migrating to a modern planning and optimisation system: CSPs are
approaching the transition to LTE by looking at it not as the single solution to the increasing data usage of
their subscribers, but as just one of the ways of providing increased high-speed data capacity. A modern
mobile planning and optimisation system should be able to handle heterogeneous network expansion of not
only 2G, 3G, HSPA, HSPA+ and LTE, but also alternative small-cell solutions (micro-, pico- and
femtocells) and Wi-Fi offload. A strong what-if capability is a must as the engineers seek the optimal mix,
but beyond that, the ability of the system to recommend the optimal mix will become increasingly
important.
Mobile CSPs should integrate their NP&O system with an NPM system. This will enable CSPs to not only
use the latest network performance data as input into the network planning processes but will also enable
optimisation automation. CSPs can benefit from improved allocation of network resources, deferred capex
investments and recouping of stranded capacity.
Mobile CSPs should consider investing in a solution that enables automation of the planning process and
more specifically the capability to simulate coverage and capacity on demand so other departments can
achieve efficiency gains.
CSPs should consider sourcing a pre-integrated NP&O and NPM solution from a single vendor. A pre-
integrated solution provides out-of-the-box functionality required to automate the optimisation process,
meaning the CSPs can benefit from efficient network planning processes from day 1.
CSPs should consider a solution with multi-vendor support. Todays CSP mobile network environment
includes equipment from at least three or more network vendors with their EMS/NMS for network
configuration and performance monitoring. CSPs should therefore consider the integrated multi-vendor
NP&O and NPM capabilities of a third-party independent software vendor to avoid siloed solution
deployment.
CSPs should consider implementing hybrid-SON capabilities in their mobile network. This will enable
them to oversee networks from multiple vendors, gluing together the edges of the two networks and
reconciling the probable two different distributed SON vendor optimisation algorithm sets.
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Analysys Mason Limited 2013 May 2013
About the authors
Dr Mark H. Mortensen (Principal Analyst) is the lead analyst for Analysys Masons
Customer Care and Service Fulfilment research programmes, which are part of the Telecoms
Software research stream. His recent interest areas include customer self-care, automation of
fulfilment processes, and data and software architecture for agile, real-time systems. The first
20 years of Marks career were spent at Bell Laboratories, where he distinguished himself by
starting software products for new markets and network technologies and architecting the
interaction of BSS/OSSs with the underlying network hardware. Mark was Chief Scientist of Management
Systems at Bell Labs, and has also been president of his own OSS strategy consulting company, CMO at the
inventory specialist Granite Systems, VP of Product Strategy at Telcordia Technologies, and SVP of Marketing
at a network planning software vendor. Mark holds an MPhil and a PhD in physics from Yale University and
has received two AT&T Architecture awards for innovative software solutions. He is also an adjunct professor
at UMass Lowell in the Manning School of Management, specialising in business strategy.
Patrick Kelly (Research Director) leads Analysys Masons Telecoms Software research
stream, which focuses on identifying the rapidly growing segments in the telecoms software
market and providing forecast and market share data on each of the 29 segments by region and
service type. He has produced research on IP next-generation service assurance, the 3G mobile
software market and customer experience management. Patrick is a frequent speaker at
industry conferences. He holds a BSc from the University of Vermont, and an MBA from
Plymouth College.
Anil Rao (Analyst) is a member of Analysys Masons Telecoms Software research team. He
has over 10 years experience in the telecoms industry, working in system integration and
service delivery with major Tier 1 mobile and fixed line operators, focusing on order
management, service fulfilment and service assurance. Anil holds a BEng in Computer Science
from the University of Mysore, and an MBA from Lancaster University Management School.
This paper was commissioned by InfoVista. Thanks to Cyril Doussau de Bazignan, Director of Product
Marketing and Juan Prieto, Product Marketing Manager Mobile Solutions at InfoVista for their briefings and
contributions to this white paper.1
1
Published by Analysys Mason Limited Bush House North West Wing Aldwych London WC2B 4PJ UK
Tel: +44 (0)845 600 5244 Fax: +44 (0)845 528 0760 Email: research@analysysmason.com www.analysysmason.com/research
Registered in England No. 5177472
Analysys Mason Limited 2013
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic,
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Figures and projections contained in this report are based on publicly available information only and are produced by the Research Division of Analysys Mason
Limited independently of any client-specific work within Analysys Mason Limited. The opinions expressed are those of the stated authors only.
Analysys Mason Limited recognises that many terms appearing in this report are proprietary; all such trademarks are acknowledged and every effort has been
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as a trademark.
Analysys Mason Limited maintains that all reasonable care and skill have been used in the compilation of this publication. However, Analysys Mason Limited
shall not be under any liability for loss or damage (including consequential loss) whatsoever or howsoever arising as a result of the use of this publication by the
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