atesio GmbH

Bundesallee 89

D-12161 Berlin

Germany

www.atesio.de

Small Cell Planning

Andreas Eisenblätter

Dario Götz

Ulrich Türke

This presentation is partially based on work and data from

the FP7 SEMAFOUR project, which receives funding from

the European Union Seventh Framework Programme

(FP7/2007-2013) under grant agreement no 316384.

LTE-1800 Macro Cellular Network in Hanover (Our Example)

1.0

0.1

0.5

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• Our example:

Region around Hannover

main station

• Dense urban, urban, rural

• Resolution of raster data

10m x 10m

• 3D path-loss predictions

• Realistic traffic distribution

based on real data

• Sampling traffic demand

every 6 min

Cell

Load

Forecasting: Evolution of Traffic Demand Over a Year on Mondays

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Analyzing Expected Network Deficiencies in Six Months Time

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Lack of Capacity in Hotspots and High Load Areas

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Coverage hole,

but little demand

Regular, temporary

need for extra capacity

Lack of capacity

at peak time

No problem for

most of the day

Enhancements options • Additional macro cells

• SON functions (MLB)

• Vertical sectorization

• Spectrum extension

• Small cells

Use Cases (SCF 049.03.01: Backhaul technologies for small cells, Dec. 2013)

• Capacity

Targeted Capacity for Hotspots

Non-targeted Capacity for QoE Enhancement

Our Set-Up

• LTE-1800 (licensed spectrum, same as macro cellular network)

• Micro cells @ 1W (vs. macro cells @ 40W)

• Capacity Extension: targeted / non-targeted

• Need to resolve temporary shortage (few hours only)

Small Cells

From © Small Cell Forum

• Coverage

Outdoor non-hotspot such as remote rural village

Indoor hotspots such as shopping malls

• Operator-controlled

• Low power nodes (femto, pico, …, micro)

• Licensed spectrum (2G, 3G, 4G, …)

• Unlicensed spectrum (Carrier-Grade WiFi)

• Range from 10m to several 100m (in some cases even 1-2km)

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Enhancing Capacity by Adding Small Cells

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Timely Activation of Small Cells

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Mathematical Optimization in Network Design, Planning & Operations

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Network Evolution

• Sampling the future

• Planning what to do and when

• Using mathematical optimization

(multi-stage stochastic optimization)

Andreas Eisenblätter, Jonas Schweiger

Multistage stochastic programming

in strategic telecommunication network planning,

Computational Management Science 9:3, 2012

Mathematical Optimization in Network Design, Planning & Operations

Geerdes: UMTS Radio Network Planning:

Mastering Cell Coupling for Capacity Optimization,

Springer-Vieweg, 2008

E., Geerdes: Wireless network design:

Solution-oriented modeling & mathematical optimization,

IEEE Wireless Communications 13:6, 2006

GreenNets FP7 Project: Energy Efficiency Optimizer

for Mobile Networks, IWSON, Dresden, 2013

E., Schweiger : Multistage stochastic programming

in strategic telecommunication network planning,

Computational Management Science 9:3, 2012

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Conclusions

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In-depth Analyses of SON Functions and Their Interplay

• MLB / MRO (Simulations with time resolution of 100ms)

• Antenna Vertical Sectorization

Network Design

• Integrated Design for Multi-layer & Multi-RAT

• Design for Energy Efficient Operations

• Optimizing for Effective Long-term Evolution

Design and Analyses for Integrated Network Management

• Policy-based Management

• SON Management

Backhaul Planning Fiber-/Copper-based Infrastructure

Small Cells

• Enhancing Capacity

• Extending Coverage