The Future of Radio Network Load Balancing White Paper

November 2011

White Paper

The Future of Cellular Radio Network Load Balancing

A special report for mobile operators and optimization engineers

White paper: The Future of Cellular Radio Network Load Balancing

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Executive summary The RF conditions in the cellular radio network were never static. For load balancing

functions in the predictable world of voice and low volume data, however, this

approximation was close enough. The introduction of smartphones and mobile data

devices brought an exponential increase in mobile data usage, in turn increasing the

unpredictability of subscribers’ demand which causes loading on network resources.

A handful of subscribers, using bandwidth-hungry services, can

push cells to congestion anywhere, anytime.

Traditional optimization methods are slow, based on long term network statistics, and

have a long turnaround time – basically changing from one static network

configuration working point to another. Iterations must be followed by a labor-

intensive verification stage. Operators who chose to maintain these methods might

find themselves facing underutilized resources, premature expansion costs to support

peak loads, and customer dissatisfaction resulting from overcrowded networks.

In order to support the changing network needs, operators require a fully integrated

automated load balancing application with a built in feedback mechanism. The radio

engineers can leave the tedious roles of manual optimization to a machine, and focus

on defining network policies, performance goals and performing radio planning

activities.

A configuration

that was optimal

early this morning

could fall short

before lunchtime

“

”


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Dealing with dynamic networks Live networks have dynamic RF traffic patterns that change throughout the week, and

over the course of a day. Changes in behavior of voice verses data usage, roads and

business areas compared to adjacent residential areas.

Unexpected load imbalances due to massive gatherings, cell malfunction or

introduction of new cells in an area, all effect the load distribution, and are rarely dealt

with as soon as they occur.

Since dealing with such dynamics is impossible from practical engineering practice

perspective, a preventive RNP (Radio Network Planning) approach is normally taken.

By this approach, cells are dimensioned to handle the busy hour traffic, regardless of

what is occurring in others cells in the area.

The high cost of imbalance

The sunk cost of underutilized resources In Static networks with no time-sharing of geographically distributed resources, costly

resources supporting peak traffic are very often unused.

In some cases, there might be underutilized cells nearby – resources that the operator

already paid for, i.e. sunk costs.

This situation is further emphasized in cases of bursty traffic demand and high

variability of the active users’ locations. Furthermore, exponential increase in demand

for mobile broadband increases this gap greatly.

Denied admissions pattern in two ajacent cells. Note that the peak load does not follow the same time pattern.

At certain times,

underutilized

resources already

paid for might be

available next to

loaded cells.

“

”


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The mobile data crunch Mobile operators today are facing an avalanche of demand, driven by the mobile data

crunch - fast penetration of smartphones and mobile broadband. The impact is

colossal. According to a mobile data usage study conducted by Cisco, an iPhone

generates as much traffic as 96 non-smart phones; a tablet generates as much as 122

non-smart phones and a laptop with a data card consumes as much traffic as 515 non-

smart phones (Cisco, 2011).

Mobile data traffic prediction by Cisco

The operational cost of networks increases to meet the increasing demands. According

to a network cost analysis conducted by Informa telecoms & media, network costs are

expected to increase by 30% in the next 2 years, and continue in this pace (Informa

Telecoms & Media, 2011b).

To support this increase in traffic would require a similar increase in resources, causing

more increase the underutilized resource gap.

Increased uneven distribution of bandwidth demands

increases this gap greatly “The top 1 percent of mobile data subscribers generate over 20 percent of mobile data

traffic, and the top 10 percent of mobile data subscribers now generate approximately

60 percent of mobile data” (Cisco, 2011). The impact of the concentration of usage on

network planners is that it is becoming increasingly difficult to predict load patterns in

both time and place.

This, in turn, magnifies the resource gap in unpredictable amounts.

A handful of

subscribers, using

bandwidth-hungry

services, can push

cells to congestion

anywhere, anytime.

”

“


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The “Empty bus” syndrome:

Expansions performed to maintain high level QoS at peak

usage times In order to support the peak traffic, radio planners’ dimensioning rules dictate adding

new cells or resources according to the peak traffic in the busy hours measured per

cell. In unbalanced networks, the load is uneven, and the busy hours are not the same

in all their cells, and there might be underutilized available resources already paid for

in the vicinity of a certain loaded cell.

Overloaded cells cause customer dissatisfaction, increased

churn, and lost revenue The option of leaving the network unbalanced, without expansion will risk

congestions, call setup failures and reduced data QoE at peak traffic conditions.

Leaving the network unbalanced without expansion will limit the data throughput

available to subscribers at peak time, lowering subscriber satisfaction, and possible

loss of revenue.

Existing solutions to handle load balancing Current network optimization processes are handled manually by radio engineers.

The granularity of existing optimization cycles is quite large, and can take days or even

weeks – thus making long term adjustments which are normally based on large scale

time averaging of traffic loading. By their very nature, such solutions can fit long term

or predicted load issues, and will, at best, provide a passible compromise between the

needs of different areas.

What types of solutions are currently available?

Decision supporting tools Using decision-supporting tool to perform the optimization calculations such as

required expansions, RF parameter changes, and the predicted impact on

performance, improves the capability of taking more inputs into consideration.

However, these types of tools provide reports – not actions, and are prone to error

due to the high degree of sensitivity to initial conditions. The radio engineers are still

left with the tasks of verifying the resulting recommendations, updating the OSS /

NMS, and checking the results. This is an open-loop solution, where the entire end-to-

end process includes manual stages to complete.

Local load balancing between carriers Some equipment vendors offer solutions of inter-frequency load balancing. These

solutions can balance loads between carriers - generally co-sectors in the same base

station. Traditional

optimization works

through big

periodical jumps

from one static

working point to

another


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These solutions, while efficient in resolving localized load imbalance cases, do not

provide a solution for a balancing the load in a cluster of cells, and require an

infrastructure of multiple carriers in each sector.

Wi-Fi or Femto offloading For local areas with consistent capacity problems, operators can elect to offload the

data portion to a local Wi-Fi or femtocell. However, according to a recent report by

Informa, in order to extract value from Wi-Fi offload mobile operators will require

carrier-grade Wi-Fi networks that are more tightly integrated into the operator’s

network and back office environments than at present, and deployment of which will

incur significant costs (Informa Telecoms & Media, 2011a).

Additionally, both Femto and Wi-Fi offloading are complex techniques which require

high level of backoffice configuration management, installation and supporting

equipment, on the network side (such as Femto Gateways) or in the UE side (the client

has to support controlled Wi-Fi offloading) etc. These reasons make those techniques

non trivial and not suitable for every operator.

In any case, this solution can only add capacity to a fixed location, in the vicinity of the

Wi-Fi AP of the Femto itself, and does not provide a solution to congestion situations

that change over time and place.

Furthermore, by passing the data to a separate network, the operator faces a potential

loss of revenue, and has less control over the QoS and SLA’s toward the customers.

With Femtos current 3GPP architecture, the operator needs to decide if to dedicate a

separate carrier for the Femto deployment (which reduces the utilization of such

carrier which could have been used in the macro network) or work in an intra-

frequency mode, which has its drawbacks in the form of need to manage the Femto –

Macro layers coexistence.

LTE offloading Another solution is offloading heavy data traffic to an LTE network. However, this

elephant-gun approach is not financially justifiable for most operators for several years

to come (Informa Telecoms & Media, 2010).

In reality, many of these solutions were not designed to contend with the degree and

extent of variation in usage that we see unraveling today. Another important factor in

the transition to LTE is the ecosystem maturity. Not only does there need to be solid

UE support for multi-mode GSM/UMTS/LTE but there has to be a viable penetration

rate of such devices to the users population in order to claim that LTE can be an

affective offloading solution. Additionally, spectrum resources are always an issue,

and, at least in the first phases of deployment, since UMTS carriers cannot be

evacuated, new spectrum will be needed to activate LTE, which means more

investments from the operator side.


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The 3G SON solution: Fully automated load

balancing To face these realities mobile operators need enhanced functionality in the existing

UMTS infrastructure that can respond to demand patterns as they form and change. A

network configuration that was optimal early this morning could fall short before

lunchtime; what is right for a certain cell could be all wrong for its neighbor.

Enhanced network responsiveness by full automation The best network engineers, working with

the finest tools can probably make no more

than 100 - 200 optimization adjustments per

month. Automated 3G SON (Self Optimizing

Network) load balancing or other

applications can do many thousands of

adjustment a day in a network, allowing the

engineers to focus on radio planning

engineering and design tasks. The endless

grunt work of tweaking, analyzing and

tweaking again is more efficiently handled

by machines.

Automated RF shaping to increase the efficiency of the

network By means of automated RF shaping, a system can modify the footprint of the

surrounding cells to the current usage demand and match the subscriber distribution

to the available resources. Using RF shaping increases the efficiency of the network,

and increases the utilization of existing resources.

Can this be truly automated? The impact of performing many changes can be disastrous if those changes are not

carefully monitored. This is the purpose of the SON’s feedback mechanism which

verifies the effect of the changes on the network.

SON revolutionizes the level of automation in operations and maintenance and

significantly decrease the OPEX associated with operations and maintenance. OPEX

Savings estimated at 65-80% (Motorola, 2009).

In order to be fully automated, a SON system must change the network configuration

in small scale, cell level iterations, verify the quality of the change performed, and

continuously compare the performance metrics to the policy targets of the operator.

The SON cycle


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Case study #1: 3G-SON load balancing lowers power

load by 20%

This case study demonstrates an activation of an RF shaping based intra frequency load balancing SON

application on a busy cluster. Activating a 3G automated load balancing application on a cluster of sites

lowered the radio resource load on the site by 20%, transferring the load to nearby sites with shared coverage.

Once the application was deactivated the load returned to the previous values.

UEs can be moved between cells by means of RF shaping – decreasing the size of the loaded site, and

increasing the size of the neighboring cells. The cells’ relative sizes can be continuously modified to fit the

current load conditions in the area covered as these conditions change.

Before load balancing After load balancing

RF shaping moves subscribers from the loaded cell to neighboring cells

0%

20%

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100%

20:00

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23:00

cluster power load

NBR #2

Loaded site

NBR #4

Average Neighbors 1,3,5

Load Balancing

activated


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Case study #2: 3G-SON load balancing lowers admission

rejections by ~100%

This case study demonstrates the ability of a 3G SON load balancing application to lower the denied

admissions. Compared to busy times on different days, when the load balancing application was activated on

the cells the number of denied admissions per PM report dropped to 0.

What to look for in an ideal load balancing application The lists below details what to look for in an ideal automated load balancing application, and how it can deal

with the load challenges of modern cellular networks

Fully automated end-to-end balancing solution, shifting traffic between cells, based on availability,

congestion, and blocking of radio resources

Support for intra-frequency load balancing in a cluster of sites as opposed to single RBS balancing between

carriers

Contains an automatic feedback method of verifying the impact of the adjustments, and correcting them if

necessary

Based on real time performance and loading conditions and not extrapolated historic averages

Rapid response time – identifying and correcting interference issues as they occur

Standard implementation and vendor agnostic solution to avoid implementation surprises.

Configurable to set performance goals and policies

An optimization cycle of minutes, rather than days or weeks

Graphic interface with real time traffic statistics and current RF conditions

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# o

f D

en

ied

Ad

dm

issi

on

s

Denied Admissions

01/06/11

08/06/11

15/06/11

Activated load balancing


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Conclusions Unbalanced networks are increasing operators’ annual costs in both lost revenue, and sunk cost of

underutilized resources. The mobile data crunch is creating sharper, more localized dynamic traffic patterns

than ever before.

The current manual solutions including decision supporting tools cannot meet the increasing need of load

balancing in terms of reaction time and accuracy.

Automated 3G-SON load balancing solutions can identify loads in near-real time, change the RF footprint of

cells to shift users from loaded cells to unloaded cells, and verify the impact on the network, all without human

intervention.

About Intucell Intucell delivers the world’s most advanced SON solutions in the market today. The company’s SON systems

are deployed by a number of leading mobile operators worldwide. Powered by real time network

visibility, Intucell’s systems automatically tune the network to actual conditions as they develop and change.

Intucell is a private international company backed by blue-chip investors, with offices in the United Kingdom

and Singapore and an R&D center in Israel.

To find out more about how Intucell can help you meet your network goals, visit www.IntucellSystems.com, or

contact our representatives at [email protected]

Sources Cisco. (2011, Feb.). Cisco Visual Networking Index: Global Mobile Data traffic Forecast Update, 2010-2015.

Retrieved from Cisco:

http://newsroom.cisco.com/dlls/ekits/Cisco_VNI_Global_Mobile_Data_Traffic_Forecast_2010_2015.pdf

Informa Telecoms & Media. (2010, November 16). LTE world. Retrieved from UK mobile broadband network

upgrade to LTE not economically viable until 2015: http://lteworld.org/uk/uk-mobile-broadband-

network-upgrade-lte-not-economically-viable-until-2015

Informa Telecoms & Media. (2011a, January 4). Mobile operators need offload to be smart and cost effective.

Retrieved from Telecoms.com: http://www.telecoms.com/23817/mobile-operators-need-offload-to-be-

smart-and-cost-effective/

Informa Telecoms & Media. (2011b, March 30). Modeling mobile broadband networks costs: LTE and offload

case studies. Retrieved from Informa Telecoms & media:

http://webinars.informatm.com/2011/03/31/modelling-mobile-broadband-network-costs/

Motorola. (2009). LTE Operations and Maintenance Strategy Using Self-Organizing Networks to Reduce OPEX.

Retrieved from Motorola Solutions:

http://www.motorolasolutions.com/web/Business/Solutions/Industry%20Solutions/Service%20Provider

s/Network%20Operators/LTE/_Document/Static%20Files/LTE%20Operability%20SON%20White%20Pap

er.pdf

http://intucell.fat-man-collective.com/technology/automation/

http://www.intucellsystems.com/

mailto:[email protected]

Documents

The Future of Radio Network Load Balancing White Paper