nsn_optimizing_stadium_and_special_venue_networks_white_paper.pdf

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Optimizing stadium andspecial venue networks with3D modeling

FutureWorksNSN White paperFebruary 2014


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CONTENTS

1. Executive summary 32. A growing demand for mobile broadband at

major venues4

3. Rapid simulation and accurate modelingcreate an e ective solution

5

3.1 Innovative NSN methodology 64. Field trial at a major US venue 65. Summary and developments 7

1. Executive summaryPlanning the best radio network for a stadium presents a uniquechallenge for operators. The propagation and interferencepatterns in crowded stadiums are highly complex, with extreme celldensities, changing line of site conditions and often, uniquebuilding architectures.

The di culty of planning is further heightened by restrictions onthe deployment of base stations and antennas because of safetyor aesthetic concerns. In addition, many stadiums use distributedantenna systems (DAS) installed by a third party.

All this makes it very time consuming and labor intensive to optimize astadiums Radio Frequency (RF) plan.

All of these problems are addressed by an innovative Nokia Solutionsand Networks (NSN) solution that allows accurate simulation ofstadium conditions and rapid con guration of a possible network.The service allows operators to test a large number of what-ifcon gurations, running into the thousands, to determine the bestcost-performance bene t for the stadium.

The NSN solution uses a full system simulation to evaluate manydesign alternatives before construction of the antenna system starts.Based on a detailed 3D model of the stadium, the solution simulatesdownlink, uplink and control channels, packet scheduling behaviorsand an accurate interference environment. The 3D model is con guredto each speci c venue, providing the expected signal propagationconditions from an antenna to a speci c seat.

A recent trial of the method was carried out for one major operator

at a leading US venue. The trial was broken down into three phases:model generation and validation, optimization of the stadium (of theexisting con guration), and a second what-if phase of optimizationwhere signi cant redesign was contemplated.

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This pilot study showed that the modeling method is accurate. It

also resulted in key lessons that will help make the stadium systemmore e cient.

Although the modeling solution has been designed for stadiums, itis likely to nd future application for other types of venue, such asairports and shopping malls.

2. A growing demand for mobilebroadband at major venues

Stadiums present a very speci c and tricky challenge to operatorsseeking to provide excellent coverage at major sports and culturalevents. With people eager to share their experiences of the event,many will be uploading more data than usual from their mobile devicesas they seek to update their social networking sites with images fromthe event.

As well as ensuring they can deliver the high capacity and high tra cthroughput required, operators face profound planning complexitybecause of the architecture of stadiums themselves, which presentmajor propagation challenges. Distributed Antenna Systems (DAS)typically employed in stadiums contain up to eight antennas per cell,requiring special expertise and tools. Remote antennas also oftenhave varying cable lengths and are located in constrained locationsthat are di cult to reach to alter manually.

Stadium networks are typi ed by very high cell densities, frequentlyhaving 25 cells per stadium, equating to a density of 500 cells per km 2(compared to 5-10 per km 2 in a dense urban center). There is alsooften a very high user density - 30,000 subscribers in a stadium is theequivalent of two million subscribers per km 2.

Together with a very unbalanced loading and usage pro le, stadiumsalso have a unique propagation environment, including challenging

line of sight conditions and a complex body loss model due to theiroften singular design. As such, they present a very high interferenceenvironment. They also demand a large capital investment to installa stadium network and o er little control over deployment optionsdue to the involvement of many operators. Venue aesthetics or safetyconstraints further restrict deployment options.

LTE runs on a single frequency which means that simply adding morebase stations to increase capacity can create areas with very highinterference and essentially zero user throughput. This can also causeoverload conditions in which the system becomes unstable. Antennasare typically down-tilted to minimize interference. But in a multi-tier stadium the interference ows down the levels and can causeinadequate performance for lower down the arena, with the resultthat people in expensive seats, often an operators most valuablecustomers, get the worst performance.

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The net result of these conditions is that stadium LTE systems do not

perform as expected and demanded. Furthermore, using distributedantenna systems (DAS) makes the situation even more complicated.Poor coverage, high call drop rates and handover failures, excessivedemotion and control channel overload all lead to attachmentfailures and a subsequent poor experience for consumers in a placewhere they expect to receive a good service. Even worse, largestadiums often host high-pro le events in the public spotlight; poornetwork performance could quickly lead to a brand-damaging loss ofreputation for an operator.

Optimizing the RF in stadiums and special venues has thereforebecome a di cult and time consuming, yet vital, process for

operators. Operators are poorly served by current networkoptimization tools when faced with the unique challenges of stadiums.Many of the planning tools available to the system engineer do notadequately assess trade-o s between Carrier-to-Interference-plus-Noise-Ratios (CINR), coverage, and capacity, especially in theuplink. Current RF-only tools fail to e ectively plan well-performingsystems and diagnose existing issues and also cannot maximize uplinkperformance, vital to meet the high tra c demand experienced duringspecial events.

3. Rapid simulation andaccurate modeling createan efective solution

The need to meet these challenges has been met by an NSN solutionthat allows rapid simulation of stadium conditions and the possiblecon gurations of a network. The service allows operators and NSNto work in tandem to hypothesize (what-if) a large number ofcon gurations to determine the best cost-performance bene t forthe stadium. Essentially, multiple antennas act together as a largeantenna to cover the stadium.

The NSN solution uses a full system simulation to evaluate manydesign alternatives before construction of the DAS starts. Basedon a 3D model of the stadium, the solution includes a simulation ofdownlink, uplink and control channels, packet scheduling behaviorsand an accurate interference environment. It also allows the analysisof various transmission modes and speci c vendor feature sets, whilefuture enhancements will simulate Voice over LTE (VoLTE).

The 3D model is con gured to each speci c venue, providing theexpected signal propagation conditions from an antenna to a speci c

seat. Measurements can be used to enhance statistical PL models,while it also o ers multiband capability.

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The simulation service optimizes antenna speci c parameters as well

as the DAS antenna feed network, minimizing expensive and longduration rework.

Such a large scale and detailed simulation requires a large computingarray to complete design alternatives in a timely manner. The NSNarray consists of over 400 processors, able to evaluate around 100designs per week, including pre/post processing and analysis.

3.1 Innovative NSN methodologyA typical project will start with a visit to the site by the operator and

NSN experts to take physical measurements of the stadium andrecord antenna limitations and feasible locations.

The next stage sees NSN generating the 3D model of the stadium.This stage can model even large stadiums with a capacity of around80,000 or more people. This will produce initial downlink-only RFplots, including SINR and RSRP for review. These are based on inputfrom the operator about issues such as the number of sectors/zones, band, tilts, beam width and transmission power.

Based on their long experience and wide expertise, NSN engineersthen optimize the system based on the outcome of the previousstage by running multiple parameter settings. Uplink analysis will beperformed at this stage.

This is followed by a second review of the optimized system. Thisincludes various cases identi ed in the rst review as well as userthroughput and capacity metrics using a Monte-Carlo simulator thatruns repeated random sampling to obtain an optimum result.

In a second optimization step, NSN experts incorporate any designimprovements, following which a de nite network design is drawn up.Future developments of the basic design can include such aspects asincreased load, LTE-Advanced features and VoLTE.

4. Field trial at a major US venueA recent application of the modeling process was that conductedfor a major US operator, which asked NSN to help it evaluate anumber of stadium networks that either were in the process of beingdeployed or were already deployed. To demonstrate the viabilityand the bene ts of the service and techniques involved, a trial wasconducted for one of the operators poorer performing venues.

DAS modeling was the most complicated part of the project dueto the large number of antennas assigned with unique physicalplacement, many of which are widely separated. A Matlab (matrixlaboratory computing environment) based tool was created togenerate the user input, tra c distribution and path loss requiredfor the simulation engines.

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Fig. 1. Tiered 3D model of the US trial venue created by mathematicalmodeling of the stadiums physical structure

Matlab was also used to combine basic geometric structures asbuilding blocks. Arc sections were used as the basis for the stadium,with 45 arc sections across 4 tiers.

The tool is unique in that only a minimum set of information isrequired to build a detailed model. For the trial venue, this consistedof cross sectional diagrams to obtain exact height and depthinformation, and arc section information obtained from Google Earthprojections. Path loss modeling was enhanced to provide a model thatdepended on the placement of the LOS, NLOS and DAS antennas.

Fig. 2. Results from phase 1 optimization of the NSN pilot for a USstadium showing a 2dB reduction in interference, equivalent to a37% reduction

System-Wide SINR Distribution

SINR (dB)

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Overall 2dBimprovement

Most Improved

3-D Stadium by Tier

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25080100120140

160180200

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Y (m) [North/South]X (m) [East/West]

H e i g

h t

( m )

Tier 1Tier 2Tier 3

Tier 4

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The trial had three phases: Model generation and validation,

constrained optimization of the stadium, and nally a second what-ifphase of optimization. In the rst phase, NSN demonstrated alignmentof simulation and live stadium Key Performance Indicators (KPIs) andalso re ned the output KPIs that the operator had speci ed.

The second phase focused on di erent aspects of the distributedantenna con gurations, including down tilt, azimuth, power, and someselected sector pairings to achieve an optimum setup. This produced37 di erent scenarios for evaluation. The key nding in this secondphase was the impact of large di erential cable loss per cell on performance.

The last phase was a large what-if analysis to evaluate variouscombinations of adjacent sectors, with the aim of assessing ifalternative con gurations could o er any gain in systemperformance. To evaluate this, a suite of over 600 unique cases wererun, none of which exceeded the best case presented in the phaseone optimization.

5. Summary and developmentsThe US pilot study shows that the modeling method is accurate. It alsothrew up key lessons that will help make the stadium system moree cient.

The conventional trial and error based system of planning andimplementing e ective stadium-wide coverage, employing a bestguess installation of antennas and a walk around to optimize them, isproving too slow and too costly for operators.

The innovative NSN solution enables network implementation ina stadium to be done correctly from the start. The rst projectsindicate that interference can be reduced by between 30-60%, withcorresponding gains in quality and throughput. NSN has provencredentials to manage large events and this solution adds further

capabilities to help address operator challenges. Following the pilotssuccess, NSN planning experts are now using this approach in furtherstadium venues in the US.

As well as stadiums, the solution could be applied to othervenue types, such as shopping malls. NSN is currently working onappropriate propagation tools and looking to bring in new venuetypes. Furthermore NSN is investigating whether this approach andmethodology can be applied to other scenarios such as macro andsmall cell deployments.

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Nokia Solutions and Networks

P.O. Box 1FI-02022Finland

Visiting address:Karaportti 3, ESPOO, FinlandSwitchboard +358 71 400 4000

Product code C401-00967-WP-201403-1-EN

2014 Nokia Solutions and Networks. All rights reserved.

PublicNSN is a trademark of Nokia Solutions and Networks. Nokia is a registeredtrademark of Nokia Corporation. Other product names mentioned in thisdocument may be trademarks of their respective owners, and they arementioned for identi cation purposes only.

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