A New Modelling Approach Is Required to Help Mobile Network

Operators Handle the Growing Demand for Data Traffic

Leonardo Lamorgese, Tomas Eric Nordlander and Carlo Mannino

SINTEF ICT, Department of Applied Mathematics, Oslo, Norway

Keywords: Optimisation, Mobile Network Planning.

Abstract: Last year, global mobile data traffic grew by 69%, and similar growth rates are expected in the coming

years. This growth affects the quality of service, and mobile network operators are finding it increasingly

difficult to manage mobile data traffic. To this end, they are drastically increasing the number of sites and

antennae, as well as modernising existing networks. This requires selecting the best antenna locations in

when extending the wireless network with new antennae, the radio-electrical parameter settings of new and

neighbouring antennae require (re)calibration to minimise interference—a process that in principle may

affect the entire network. Moreover, the antennae must connect to the core network and influence it. This

complex optimisation planning problem does not lend itself well to a manual solution approach. Still, these

plans are developed “manually”, with the support of IT tools, through a time-consuming and inefficient

trial-and-error process. Applied optimisation is needed to tackle this problem effectively, but this requires

advancing the state-of-the-art: Most papers focus on solving the different sub-problems independently.

However, these affect each other heavily and they must be considered simultaneously to maximise the

offered service: optimising the location and configuration of new antennae and the configuration of wireless

network radio-electrical parameters, while taking into account access to the core network.

1 INTRODUCTION

Last year, global mobile data traffic grew by 69%

(CISCO, 2015), and similar growth rates are

expected in the coming years

. This growth affects

the quality of service, and mobile network operators

(MNOs) are finding it increasingly difficult to

manage mobile data traffic during peak times

(Rivanda, 2015). The telecom community expects

that MNOs will need about 1,000 times today’s

capacity to handle the demand in 2020 (NGMN,

2015). Consequently, MNOs are trying to access

additional bandwidth, which is a scarce and

congested resource. Moreover, MNOs are drastically

increasing the number of sites with antennae, as well

as modernising part of the existing network.

These are challenging planning tasks. To satisfy

evermore-common increases in demand, planners

are required to select appropriate locations for

installing new base stations and to configure radio-

Mobile data traffic is expected to grow at a compound annual

growth rate of 57% from 2014 to 2019 (CISCO, 2015).

electrical parameters

both of the new and pre-

existing antennae. This involves finding efficient

locations among a large number of candidate sites,

while considering service area coverage, spectrum

availability, installation costs, demographics, etc. In

Figure 1: Wireless and backhaul network.

E.g. power emission, transmission frequency, tilt, height, antenna

diagram or type and many more.

402

Lamorgese, L., Nordlander, T. and Mannino, C.

A New Modelling Approach Is Required to Help Mobile Network Operators Handle the Growing Demand for Data Trafﬁc.

DOI: 10.5220/0005814904020407

In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems (ICORES 2016), pages 402-407

ISBN: 978-989-758-171-7

addition, when extending the wireless network

with

new antennae, the antennae may interfere with each

other, and the parameter settings of neighbouring

antennae might need recalibration to increase

coverage while trying to minimise interference.

Furthermore, the wireless network must connect

to the core network, via backhaul connections

(wireless, copper or through fibre optic cables) (see

Figure 1). The set of connections in the backhaul

must be adjusted whenever changes in the wireless

network take place or when traffic volumes increase.

Clearly, all above planning decisions affect each

other and they should be taken jointly to maximise

the offered service. Access to the backhaul network

in particular is expected to become a major

bottleneck in the coming years (NGMN, 2012). With

the drastic increase in the number of sites/antennae,

the problem of choosing the best sites, calibrating

the radio-electrical parameters and establishing low-

cost backhaul connections might need to be solved

several times per month for a given area. This is a

complex combinatorial optimisation problem where,

for even a small version with few antennae, the

amount of possible combinations of parameters and

decisions quickly exceeds what a human can

evaluate manually. Infrastructural investments,

whether installing antennae or new connections to

the backhaul network, are generally very costly, so

planners are put under great pressure by MNOs to

minimise overall capital and operational

expenditures. Today, this planning task is carried out

by specialised personnel (mobile network planners)

in a time-consuming and inefficient trial-and-error

process. Most existing planning tools only present

functionalities to assist in the manual planning

process. To our knowledge, the few tools that do use

optimisation techniques for automated planning only

handle simplified versions of the problem (e.g. fewer

parameters).

The paper is organised as follows: Section 2

provides a brief literature background on the

optimisation of mobile network planning. Section 3

describes the current approach used by MNOs and

its limitations, our arguments for using optimization

in the planning process and the research challenges

that have to be overcome to do so. Section 4

concludes the position paper.

Normally, this part of the mobile network is called the wireless

access network. For the sake of simplicity, we drop the term

“access” in this paper.

2 BACKGROUND

The literature on optimisation applied to the design

of wireless and mobile networks is very wide. It

dates back to the late 60s/early 70s, when the first

works on frequency assignment appeared. The merit

for introducing mathematical optimisation and graph

colouring techniques for this type of problem is

usually credited to (Metzger, 1970). Since these

early works, devoted to frequency assignment, the

focus has widened to encompass all the

geographical, physical and radio-electrical

parameters of wireless networks. These may include,

e.g. antenna location, tilt, height, transmission

frequencies, power emission, polarisation, diagram,

etc.

Traditionally, the design of a mobile network has

been decomposed into two major sub-problems:

● Wireless Network Design (WND). This can

be summarised as the problem of 1) selecting

appropriate locations for new base stations of a

wireless network and 2) establishing radio-

electrical parameters of the old and new

antennae and assigning radio channels so the

demand of service is satisfied and the overall

installation and operational costs are minimised

(e.g., Capone et al. 2006).

● Backhaul Network Design (BND). This

consists of choosing the cheapest way to link

the antennae to aggregation nodes (assumed

already available), which provides the interface

to the core network. Connections must meet

quality of service and survivability

requirements (Charnsripinyo and Tipper,

2005). Choosing the most adequate links will

allow MNOs to sustain traffic and avoid the

risk of seeing the backhaul become the

bottleneck of the network (NGMN, 2012).

This conceptual subdivision is reflected both in the

practice and in the literature of mobile network

planning. The literature abounds with models and

solution techniques for WND and BND (treated

independently), which are both very difficult

combinatorial optimisation problems. Since the early

1980s, several optimisation models have been

developed to tackle WND (see Capone et al., 2006),

at the time considered the bottleneck problem. The

benefits of bringing these developments into practice

were already pointed out in (Ceria et al., 1999). In

2005, Dehghan stated that the use of automatic- and

optimisation-oriented planning techniques might

lead to a cost reduction of up to 30%. Even if very

limited, experiences show that the exploitation of

optimisation techniques may produce significant

A New Modelling Approach Is Required to Help Mobile Network Operators Handle the Growing Demand for Data Trafﬁc

403

increases in coverage (see e.g. Atesio, 2000 and

Mannino et al., 2006). Typically, WND is further

decomposed into two sub-problems (Capone et al.,

2006): coverage planning, devoted to the choice of

antenna localisation and radio-electrical parameters,

and capacity planning, often denoted by frequency

assignment (for a survey, see Aardal et al., 2007),

where one wants to find an optimal assignment of

radio channels to minimise overall interference. A

successful attempt to combine the two sub-problems

into a unique optimisation task was carried out in

Mannino et al. (2006) for broadcasting networks and

in D’Andreagiovanni and Mannino (2009) for

WIMAX (Worldwide Interoperability for

Microwave Access) mobile networks. From the

existing literature, the successful approaches to

WND often combine heuristic frameworks with

mathematical optimisation, with emission powers

represented by a continuous variable (as in Amaldi

et al, 2006) or, more recently, by binary variables (as

in D’Andreagiovanni et al., 2013). BND is typically

modelled with mathematical programmes (e.g.

Charnsripinyo and Tipper, 2005; Cox and Sanchez,

2000; Islam et al., 2015; Wu and Pierre, 2003).

These models are then solved either heuristically or

by using exact methods for small instances, as in

Grøndalen et al. (2015). Few authors have attempted

to solve WND and BND as a joint optimisation

problem (e.g. St-Hilaire and Liu, 2011). In general,

such attempts are limited to so-called metaheuristic

approaches, without guarantee on the quality of the

solutions produced.

3 TIME TO MODEL THINGS

DIFFERENTLY

Current Approach and its Limitations. Today,

mobile network planners perform their planning

tasks ‘manually’, usually assisted by the available

commercial tools; however, these only provide very

basic support. From our experience, our literature

research, and discussion with Teleplan Globe AS

and Telenor

the planners’ current workflow can be

summarised as follows: they iteratively identify

possible locations for antennae, simulate their

instalment and adjust the parameters of the new and

pre-existing antennae accordingly. While choosing

Provider of planning software for MNOs (http://

teleplanglobe.no/)

Norway's main mobile network operator (http://www.

telenor.com/about-us/global-presence/norway/)

locations, they also check for possible backhaul

connections. This time-consuming task is repeated

until an acceptable result is reached. As mentioned,

finding the best combination of location and

parameter values for all of the network antennae is

actually a very hard optimisation problem.

Furthermore, the locations chosen should allow a

(possibly least-cost) backhaul connection. The sheer

number of possible decisions to explore makes a

‘manual’ approach clearly inefficient. Moreover,

such tasks will in any case soon become impossible

for the current workforce to carry out, given that the

rapid increase in smaller cells (antenna coverage)

constantly requires the installation of new antennae

and the re-configuration of network parameters.

Need for Applied Optimisation. Some planning

software providers claim that they embed

optimisation in their tools. To our knowledge

however, the tools used by practitioners have limited

or no actual optimisation functionality. In addition,

such tools never address the planning of the

backhaul and wireless network jointly.

Optimisation-based planning tools could

substantially reduce the planning time and provide

solutions that are more efficient, allowing MNOs to

keep up with market demand and to enforce their

customer policy at minimal costs, despite the sharp

increase in demand. Such tools would also be

beneficial for the public in general: their use would

result in better coverage, increased capacity, reduced

interference and reduced outages of the selected

sites. Moreover, improved service could be crucial

for critical/emergency services that require a stable

connection. Optimising the layout of network

elements and minimising interference will also lead

to cleaner signals, where the energy usage is

minimal for a given quality of service. Additionally,

the predictability of the network will lead to reduced

operational costs for the MNOs. The savings could

be reinvested into installing new antennae and, in

general, into improving and modernising the

network’s infrastructure. This positive externality

will in turn contribute to improve further the quality

of service and to connect a higher number of users.

However, the limitations in existing systems are

rooted in the status quo of related research. Indeed,

the state-of-the-art in optimisation applied to

telecommunications must be advanced to overcome

these limitations and to achieve the above

improvements.

Applied Research Challenges. In the optimisation

literature (section 2), most approaches have either

tackled somewhat stylised problems or focused on

ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems

404

specific sub-problems. To have a substantial impact

on the current practice requires designing richer

models and algorithms that are more effective for

the overall problem faced by mobile network

planners. To interact with the planning process and

to be used in practice, such algorithms should be

capable to provide good or optimal solutions to the

overall problem very quickly. More specifically, the

following parts need to be planned jointly:

● The location and configuration of new

antennae (WND).

● The configuration of wireless network

radio-electrical parameters.

● The connection to the core network (BND).

Addressing these effectively requires facing critical

modelling and algorithmic challenges. A unifying

mathematical model to combine WND and BND

must be devised, along with effective algorithmic

schemes to solve the models in a reasonable amount

of time for real-life scenarios. This is a clear

challenge, as, to our knowledge, none of the

methods presented in the literature has ever actually

been applied in a real-life planning process. In

summary, the following advancement in the state-of-

the-art for WND/BND will be needed:

● Novel unified models for the joint

optimisation of WND and BND.

● A fast and effective algorithmic framework

for solving the unified model in a

reasonable amount of time and producing

good or optimal solutions for real-life

instances.

● Modelling to tackling potential planning

scenarios, including different wireless and

backhaul techniques, topologies and a large

numbers of parameters.

Our community must focus on designing such

effective integrated approach. First, this requires

defining a strong mathematical formulation for the

joint WND and BND problems. To this end, a

possible approach is to build upon the pure 0,1

formulations for WND introduced in

D’Andreagiovanni et al. (2013) and to extend them

to cope with joint WND and BND. This allows for

the computation of bounds on the optimal solution

values and helps to assess the quality of solutions at

hand. Second, effective algorithms to solve the

unifying model must be developed. Due to the well-

known computational difficulty of the problem, new

decomposition techniques in conjunction with

classical row-and-column generation schemes must

be considered. These algorithms could include

approximate methods, such as meta-heuristics, to

produce quickly good quality solutions that could be

used as initial feasible solutions to the problem or,

indeed, as final solutions when other methods fail to

improve on these. The ability of optimisation

algorithms has increased dramatically during the last

decades, mainly due to the improved methods and

the increased processing powers of personal

computers (PCs). (Bixby, 2002) argued that

algorithmic and software improvements have played

as large a part as processing power

when it comes

to solving large linear programmes faster. During

the period 1987 to 2000, Bixby estimated a speedup

increase of six orders of magnitude in solving

power, where processing power and memory

contributed by three orders of magnitude. The

remaining three orders of magnitude is due to an

improved algorithm: “A model that might have

taken a year to solve ten years ago, can now solve in

less than 30 seconds”. Recent developments in

hybrid, parallel and heterogeneous computing could

allow us to overcome the computational challenges

encountered when moving towards the integration of

the planning problems. For instance, Graphics

Processing Units (GPUs) can be exploited as

computational power. In addition, most PC-based

optimisation algorithms use sequential optimisation

methods, which was not an issue while we had an

exponential increase in processor clock frequency.

However, the modern PC architecture is parallel and

heterogeneous—its multiple cores, programmable

GPUs open up for parallel computing and

heterogeneous computing. Hybrid optimisation

methods, as mentioned above, lend themselves well

to parallelisation, and these have been successfully

applied to solve a number of large scale optimization

problems (Brodtkorb et al., 2013). A possible line of

research to investigate is the use of heterogeneous

computing to plan mobile networks by solving

WND and BND jointly.

A final research question concerns the so-called

self-optimising network (SON). SONs are functions

that allow the network to react to fluctuations in

traffic demand by adjusting antenna parameters in

real time to hand over capacity where needed.

Thanks to this, the network can adapt to serve

several different demand scenarios. This feature is

neglected by current planning models. However,

considering SON capabilities in the optimisation

model would allow the design of better performing

and less costly networks. We believe that one way to

incorporate SON capabilities into planning models is

by means of recoverable robust optimisation

models. Recoverable robust optimisation has been

The increase in memory is also beneficial for the algorithms.

A New Modelling Approach Is Required to Help Mobile Network Operators Handle the Growing Demand for Data Trafﬁc

405

recently introduced in (Liebchen et al., 2009), where

they consider several input scenarios along with an

input algorithm capable of partially recovering

deviations from the nominal input. SON capabilities

can thus be interpreted as recovering algorithms.

4 CONCLUSIONS

The rapid growth rates in global mobile data traffic

impacts the quality of service, and MNOs are

finding it increasingly difficult to manage this

traffic. They are drastically increasing the number of

sites, antennae and modernising existing networks—

this is a combinatorial planning problem, where

optimisation-based tools could provide substantial

assistance. Today however, the planning process is

still “manual”. Existing optimisation literature have

either tackled stylised problems or focused on

specific sub-problems. To provide an optimisation-

based planning tool to effectively assist planners, the

optimisation community needs to work with richer

models and algorithms that tackle the overall

problem faced by mobile network planners. This

requires rethinking current approaches. We need to

optimise jointly the location and configuration of

new antennae, the configuration of wireless network

radio-electrical parameters and the connection to the

core network. New models have to be designed,

supported by effective algorithms that fully exploit

recent improvements both in methods and hardware.

The potential benefits of using such optimisation-

based planning tools reach further than just MNOs—

it will have a positive impact on society as a whole.

ACKNOWLEDGEMENTS

We would like to thank Ola Aanstads at Teleplan

Globe AS and Vegard Tingstad at Telenor Norge

AS, and Isabelle Catherine Rebecca Tardy at

SINTEF’s department of Communication Systems

for the discussion and their opinion on this topic.

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