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Amit Mukhopadhyay, Ph. D.

Director, Network Modeling & Optimization - Wireless

Bell Labs

Self-Organizing Networks in LTE and Quantification

of Economic Benefits

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Agenda

• Self Organizing Networks (SON) fundamentals

• Economic model

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Self Organizing Network (SON) is a 3GPP-standardized initiative which drives the

building of intelligence and automation into the network itself to help operators solve

those challenges

SON provides Operators with a path to maximize their network performance with

lower effort (and thus lower cost).

3GPP SON helping Operators solve their challenges

Increase network quality

OPEX reduction

Fast adaptation to network conditions

• Provide Higher End User Quality of Experience

• Ensure service continuity

• Embed system and product expertise in the network

• Avoid time-consuming and repetitive tasks

• Avoid drive tests

• Process simplification

• Avoid error-prone and slow manual operations

• world of data is dynamic so networks must adapt real time

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EPS Architecture: eUTRAN and EPC Network Topology

Standards described the EPC elements as logical functions that can be integrated or distributed

- Physical topology & implementation varies based

on numerous considerations

Standards specified that eNBs are inter-connected via the X2 reference point to improve handover performance

- X2 connectivity is logical, not necessarily

physical

- Many options of creating logical connectivity, with

different cost implications

eNB typically connects to multiple MME’s and S-GW to leverage load sharing / balancing capability and increases reliability

MME

eNB eNB

eNB

MME

SGW

X2

X2X2

SGW

PCRF

PDN GW

PDN GW

S1-MME S1-U

S1-US1-MME

S1-U

S1-MME

S11

S7c

S5/S8

S7

eUTRAN

EPC

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SON Overview

� Self Configuration :

• Automatic installation procedures to get the necessary basic configuration for system operation.

• Works when the eNB is powered up and has backbone connectivity but the RF transmitter is not yet switched on.

• Functions: Basic Set up + Initial Radio Configuration

�Self Optimization

• UE & eNB measurements and performance measurements are used to auto-tune the network.

• Works when the pre-operational state is complete and the RF interface is additionally switched on.

• Function: Optimization/Adaptation

Fig 22-1-1: 3GPP TS 36.300

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Example Prelaunch and Post-launch Optimizations

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Design Configure

Drive Test

Analyze DT Results Resolve/Reconfigure

Pre-Launch

PM Collect Data (Remotely)

PM Analyze

Top 10

Analyze DT Results

Resolve/Reconfigure

Post-Launch – Continuous operations

RFBackhaulOther

Don’t know/DT requiredField Visits Remote ly Resolvable

Drive Test

Repeat until desired configurations achieved

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Key SON features

• Self Configuration

- Network connection, database update, software download,…

- Physical Cell ID configuration

- Neighbor Recognition

• Self Optimization

- Load balancing

- Hand-off optimizatyion

- Interference co-ordination

- Capacity and coverage optimization

- Energy optimization

• Self Healing

- Outage detection and compensation

- Multi-homing

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Physical Cell ID configuration

• Cell PCI needs to be:

- Collision free

- Confusion free

• Only 504 unique PCIs available, which need to be recycled among cells

• Determined during planning via planning tool and then configured into eNodeB via EMS or manually/config files

New cell

PCI

algo

Automate configuration efforts

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B

A

Automatic Neighbor Recognition (ANR)

As a part of the normal call procedure, the eNB instructs each UE to perform measurements on neighbor cells.

1.The UE sends a measurement report regarding cell B. This report contains Cell B’s PCI.

2.The eNB instructs the UE, using the newly discovered PCI as parameter, to read the ECGI

3.When the UE has found out the new cell’s ECGI, the UE reports the detected ECGI to the serving cell eNB.

4.The eNB decides to add this neighbor relation

Reduce pre-launch and post-launch configuration efforts

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LTE Load Balancing

� Objective: Distribute UEs camping on or connected to a cell to balance the traffic load.

� Delay or advance the handing over of UEs between cells.

� eNB monitors load in the cell and exchanges related information over X2 or S1 with neighbouring node(s)

Load Balancing

Distribute load across neighbouring cells

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LTE SON Vision:

Inter-RAT Load Balancing for Layered Heterogeneous Networks

1. 3G-only UE: stays on 3G as it moves through network

2. 3G+LTE UE: moves between 3G macro, LTE macro, & LTE macro/pico

3. 3G+LTE UE: able to access 3G & LTE macros, plus LTE HeNB (femto)

LTE Macro

Partial LTE deployment

(midterm transition stage)

3G Macro

Ubiquitous 3G deployment

GeographicLayout

LTE Pico + Femto Femto

Pico Femto

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3

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Robustness Optimization

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• Objective: detecting and enabling correction of following problems:

- Connection failure due to intra-LTE mobility

- Unnecessary HO to another RAT

• HO failure categories

- Failures due to too late HO triggering

- Failures due to too early HO triggering

• Requirements:

- Detection of too late/early HO or wrong cell HO

- Reducing inefficient use of network resources due to unnecessary HOs

Identify failed/unnecessary HOs and suggests changes in HO parameters

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Coverage and Capacity Optimization

• Objective: Users’ ability to establish and maintain connections with acceptable or default service quality

• Implies:

- Coverage is continuous

- Users are unaware of cell borders

• Coverage includes both idle and active modes for both UL and DL

• Coverage optimization has higher priority than capacity optimization

• Optimization of parameters through analysis of call trace data

• Also used for drive test minimization

- Requires UEs with GPS and data logging

- Privacy concerns “not applicable”

Trade-off between capacity and coverage

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Inter Cell Interference Coordination (ICIC)

- Implemented to improve interference-limitations found in Cellular systems deployed with universal reuse

- Inter Cell Interference Coordination

- Uplink – Soft Fractional Frequency Reuse, Fractional Frequency Reuse

- Downlink - Power limitation on frequency blocks

- Improvements in later releases

- Antenna parameter optimisation e.g. tilt, azimuth

Improve user throughput and customer experience

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Energy Consumption Optimisation

• Drivers towards a Greener network – Reduce Carbon Footprint

• Full eNode B switch-off is not possible: idle mode insteadResponsiveness � Component level (CPU, FPGA)

� Board level (modem)

� Cell level (Carrier)

� Site level (Node B)

Reduce Power consumption; additional benefit - Interference Reduction

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Automatic Cell Outage Detection and Compensation

Two tasks:

�Detect Cell Outage

� In eNB with mis-operating cell

� In surrounding cells of neighbour eNBs

�Compensate for Cell Outage

� Fault report to OA&M

� From affected eNB

� From neighbour eNBs

� Reconfig of antenna parameters

� Azimuth and tilt adjustment via RET

� Reconfig of neighbour power

� Coordination between affected eNBs

Minimize system capacity loss following outage

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Interactions between various SON use cases

Self configurationICIC

Hand Over

optimization

Load Balancing

optimization

Capacity

Optimization

Energy

Savings

RACH

optimization Cell Outage

detection/comp

Coverage

Optimization

Automatic

Neighbor Relation

Physical Cell ID

configuration

Terrain

OPEX impact

Coverage impact

Capacity impact

LEGEND

2G/3G/4G

Use case Interactions must be modeled and quantified to realize full SON potential

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Lifecycle Cost Impacts of SON

Network Planning

Site Acquisition &

Preparation

Physical Installation

Commissioning

Integration

Pre Launch RF

Optimization

OA & M

Indicates SON impact

Pre-Launch Activities: Onetime CapEx

Post-Launch Activities: Recurring OpEx

Several Pre- and Post-launch Activities Leverage SON Efficiencies

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CapEx Elements Impacted by SON

RAN

Core

Transport

Equipment

Deployment Services

Site Construction

Services not impacted

by SON features

Services impacted

by SON features

Study

focus

Network CapEx RAN CapEx Deployment Services CapEx

A Relatively Small Portion of Network CapEx is Impacted by SON

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OpEx Elements Impacted by SON

CPGACPGA

CCPU

CCPU

CPGACPGA

CCPU

CCPU

Study

focus

A Relatively Small Portion of Network OpEx is Impacted by SON

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Mapping of Key SON Features to Business Impacts

Note: Time to Market (TTM) and Churn Reduction impacts have been left out

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Computation of Key Cost Elements

Note: Only CapEx and OpEx included in this analysis, not Marketing Costs

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Key Modeling Assumptions

• Effort estimate parameters:

− Scheduled over a 5 year period implementing > 10,000 sites

− 2.5 million subscribers at the end of 5 years

− ~ 90% of current 2G/3G cell sites reused

• Unit labor cost estimate parameters:

− Typical wireless operator requires 900 and 1200 employees to manage RAN capacity and service assurance,

− Average annual loaded salaries of ~ $100,000

• Energy cost estimate parameters:

- Cell sites in business centers and dense urban areas are often lightly loaded

- 10% of sites in these areas could be powered down overnight

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CapEx and OpEx Savings Components

Relative % reduction of CapEx Activities due to SON

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Network Planning Commissioning Integration Pre-launch RF

Optimization

% CapEx Without SON

%CapEx With SON

Relative % reduction of OpEx Activities due to SON

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Power OA&M

% OpEx Without SON

%OpEx With SON

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Concluding Remarks

60.37%47.59%

39.63%

25.99%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Without SON With SON

Capex % Opex %

•A relatively small part of Network CapEx and OpEx benefit directly from SON; but cost areas where SON is impactful, the benefits can be significant

•SON benefits for an operator scales linearly with the size of the network; future mini/pico cells may bring benefits of SON in a more significant way

•Impacts of SON are also reflected in faster deployment and better QoE which translate to Time To Market as well as churn reduction benefits

•Future SON and xSON features may open the doors for unforeseen benefits.

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High Traffic Density Area

Serving Cell/Sector

Normal Traffic Map

γ α

β

γ α

β

γ α

β

γ α

β

γ α

β

γ α

β

γ α

β

BS I BS III

BS II

γ α

β

γ

β

γ α

β

γ α

β

BS I BS III

BS II

γ α

β

γ α

β

α

γ α

β

Serving Cell/Sector

Has Outage

BS II shared load

A Final Thought: Outage Detection and Self Healing

γ α

β

γ

β

γ α

β

γ α

β

BS I BS III

BS II

γ α

β

γ α

β

α

γ α

β

Dynamically minimize service interruption caused by cell outage based on real timetraffic loading. Adjust antenna configuration in nearby cells.

High Traffic Density Area

Load is shared by

The Neighboring cells BS I, II & III

⇒Minimize impact of cell outage

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Acknowledgments

• Craig Connick, Kamakshi Sridhar, Susan Sanders, Louise Gabriel, Ashoke Sharma and many other colleagues at Alcatel-Lucent