Smart network planning for MBB
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Fast-growing mobile data services and smart devices have brought operators a large
number of new subscribers and a significant increase in network traffic, but the revenue
has not grown proportionately. And when they look to new business models as a way
out, they are often plagued by the signaling storms and unpredictable data traffic
changes caused by smart devices and the applications running on them.
The root causes
Industry studies and estimates suggest that, in the future, traffic from a smart device
may be 10 to 100 times higher than that from a feature phone. This requires operators to
make enough network investments to meet ever-increasing network capacity
requirements. From a global perspective, even leading operators have suffered
unexpected network overloading to different extents over the past couple of years, even
after spending heavily on their networks. The overloading can occur at the wireless side
or the core side. Nonetheless, it is impossible for operators to increase network
investments year after year without an end.
The traditional method for network planning, based on service traffic growth only, has
failed to solve this industry dilemma for the following reasons:
Before smart devices appeared, devices behaved in a relatively unified way, as services
running on them were voice services and simple data services. As a result, the traffic
model on the network equipment side was relatively stable during a given period of
time. When planning networks by predicting network development trends, operators
had only to focus on the volume of transmitted data based on the number of subscribers
at all sides other than at the wireless side, where they had to consider coverage and
interferences. Nonetheless, smart devices have brought numerous, unpredictable
changes, such as signaling width, the maximum numbers of concurrent connections of
numerous new services, changes in the data throughput of every data service
connection, and the background traffic of new services.
As previous networks had only one variable – the subscriber count, network planning
was simple and could be easily done by using this function: F (future demand for
network capacity) = current network capacity + the increment of network capacity (in
proportion to subscriber growth). Today, smart devices bring with them diverse
behaviors and services. Therefore, a method is needed that can recognize changes in the
subscriber count, devices, and services and that can predict network resources
development for network planning: F (future demand for network capacity) = current
network capacity + the increase in network capacity (in proportion to subscriber growth)
+ the increase in network capacity (smart devices/service growth).
It is obvious that current network planning should be based on the projection of changes
in the traffic model brought by the subscriber count, devices and services. However,
how to visualize the impact of smart devices and services on the capacity remains a
problem.
First of all, the visualization method must be able to predict dynamic changes in the
traffic model brought by smart devices and services – changes in the equipment’s
traffic carrying capacity. This capacity depends on end-to-end network equipment
resources, whose theoretical limit is the scenario where different types of traffic are
most effectively reused on the equipment’s software and hardware resources. The
traffic model consists of parameters that represent statistics of the performance and
times of use of different resources on network equipment and that indicate the actual
utilization of the existing equipment’s specified capacity. Smart devices have brought
about many parametric changes that lead to dynamic changes in the traffic model for
network equipment and, consequently, in the equipment’s ability to support resources.
Any failure to find resource bottlenecks in the changing network in a timely manner
may lead to unexpected network overloading.
Secondly, the method must be able to match changes in the traffic model to the
end-to-end capacity of network equipment, recognize weaknesses, and perform
precision network planning, thus answering the two critical questions that operators
have to face when predicting long-term network development: When should timely
investment be made? How can network capacity expansion be planned properly?
Practice
In December 2010, operator D in country G began working with Huawei to design a
solution addressing those challenges. It was called the MBB capacity service solution,
comprising two major parts.
Prediction of the traffic model on the network: This part correlated service
development with traffic model development and predicted quantitative changes in the
traffic model in the following two steps.
Visualizing the impact of smart devices and services on the network: Based on the
analysis of its historical network data, the project team studied the impact of the
addition to operator D’s wireless nodes B, the enablement of the CELL_PCH feature
and the use of smartphones on the traffic model (traffic model) for the equipment.
Predicting the traffic model for PS equipment of the core network: The project team
predicted the traffic model for its core network equipment in 2011 based on operator
D’s business development plan for the year and in combination with an analysis of its
existing networks and a database about the relationship between smart devices and the
traffic model that had been created using Huawei’s global experience.
Test of network resource capacity: This part correlated the traffic model with the
capacity of equipment resources and provided recommendations for network
adjustments needed to satisfy future capacity requirements, which also involves two
steps.
The first step was to analyze future equipment capacity requirements. Operator D faced
complex changes in its actual network traffic. As networks are gradually going IP, there
is no precise, fixed algorithm that can translate the traffic model into equipment
resource utilization. The project team tested every piece of SGSN equipment in the lab
environment, ascertaining future equipment capacity requirements – a best industry
practice for increasingly accurate calculations of the traffic model.
The second step was to identify the requirements for equipment resource capacity. The
project team predicted correlations between equipment capacity and resources through
the traffic model before formulating operator D’s development plan for 2011, which
answered the questions of whether its existing network platform could support its
business development and when it would encounter capacity bottlenecks. As a result,
the operator was able to know in which category of network resources overloading
would take place, when would be the best time for network adjustment, how network
capacity expansion could be planned properly, and when a timely investment should be
made.
By analyzing root causes and effects of actual changes of smart devices and services in
mobile data networks through the above steps, operator D successfully predicted the
traffic model for its networks and equipment and developed plans to address the future
use of equipment capacity, while minimizing the risk of abnormal network
overloading.
Outlook
With the ongoing development of smart devices and services, operators have come to
realize that their plans for future services need to allow for the impact of these services.
Our experience from this operator D project suggests that operators need the following
critical capabilities in this respect:
Capability of visualizing network-wide devices and services. The ability to
accurately understand the behavior of mainstream devices and services in a real
network environment is the basis for assessment in daily monitoring. Therefore
operators particularly need the ability to visualize the changes in the traffic model
before and after network adjustments as well as different effects of devices and services
on such changes.
Capability of business development planning and analysis based on devices
and services. When planning services, operators need to think about device and
service plans in addition to traditional subscriber growth. Ideally, they are able to break
their business development plans down into device and service development in every
major area in combination with daily device and service visualization.
Capability of designing network traffic control policies. In network investment and
construction, equipment resources must be maximized whenever possible before the
overall profitable business model for mobile data services becomes clear.
In addition to conventional capacity expansion design and differentiated market
strategy development, operators need to consider the QoS plan and design. Also, they
need to take into account the application scenarios for service QoS use and scheduling,
and control policies in an end-to-end way so as to realize differentiated scheduling of
subscribers, devices, and services in the network.
Network planning based on smart devices and the traffic model is one of the top
priorities for mainstream operators. As market strategies and actual network
environments vary from region to region, implementation schemes require
customization. Huawei is willing to partner with operators to explore best practices in
this regard.
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