CASE STUDY

www.qualcomm.com/esg

CASE STUDY

Delivering technical evaluations and services to operators

Qualcomm Corporate Engineerings ESG helps operators to manage network coverage and capacity more effectively using their proprietary QUEST simulation tool.

SITUATION

Performing indoor network coverage and capacity analysis The operator lacked tools for predicting data traffic at a high attendance sporting event and looked to Qualcomm ESG for an independent, third party network coverage and capacity analysis. During non-game days network traffic remained idle, but increased dramatically during sporting events. Twenty thousand subscribers were expected on game day, each using their cells phone to make calls and access data. There are planning tools for designing outdoor networks to handle data traffic surges; however tools for analyzing indoor environments such as stadiums, convention halls, or concert halls typically dont exist.

Operators can benefit from using QUEST, a simulation tool that accurately predicts network coverage, capacity and data traffic for indoor environments. QUEST uses real-world network data and generates network coverage and capacity results for a given mix of inputs (such as morphology, user equipment, traffic patterns, and/or applications).

CHALLENGE

Predicting usage surges & anticipating coverage constraints A global wireless operator based in North America anticipated a surge in network traffic during a major indoor sporting event and wanted to improve its network coverage and capacity. They engaged Qualcomm to optimize network capacity and deliver an improved user experience.

SOLUTION

Forecasting data traffic to improve Qualcomm collaborated with the operator to run the QUEST steps depicted in Figure 2 and provided technical consulting and analysis including::

u RF optimization at indoor sites and surrounding macro sites

u Throughput estimates based on network traffic and operator assumptions

u Parameter recommendations to manage network traffic during the event

u Planning and stress test support at the event

SITUATION

u Anticipated a surge in network traffic during a championship game at a large, indoor stadium

u Required capacity and coverage dimensioning for a large indoor event

u Limited time and resources did not allow for stress tests

Figure 1: Managing Data Capacity Constraints

PREDICTING COVERAGE AND SIMULATING NETWORK DESIGN WITH QUEST

COMPANY

- Operator in North America

- Nationwide deployment of UMTS/HSPA

Capacity Constraints &Network Not Optimized

Collect data on existing network configuration, traffic forecast

Network Optimized

Run QUEST

Determine network constraints and limitations based on QUEST

Recommendations

Improvementsto network

configuration (e.g. RF

configuration,

parameter

settings)

Improvements to

dimensioning process

Networkplanning

tools

CASE STUDY

www.qualcomm.com/esg

CASE STUDY

Cell 0

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QUESTACTUAL

HS

DP

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hro

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hp

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Cell 1Cell 2

Cell 3

RESULTS

Using the QUEST simulation for capacity planning

Qualcomm ESG used their propriety tool to estimate cell capacity before and after antenna optimization. As shown in Figure 3, QUEST can accurately predict actual HSDPA throughput traffic. QUEST helped predict the operators RF needs and required distributed antennae systems (DAS). Using recommendations from Qualcomm ESG, the operator determined optimal antenna placement for providing uniform coverage and sufficient capacity.

2012.04.V01

RESULTS

u Minimized call drops

u Improved coverage

u Optimized data throughput and managed capacity

u Potential CAPEX and OPEX savings

FIGURE 2: QUEST simulation tool

FIGURE 3: Example of capacity prediction at an indoor event

HSPA Code, Power, HS-SCCH Number

UE Categories, Types of UE Receivers

64 QAM, MIMO, DC-HSPA

Scheduler Capabilities, QOS

Rise over Thermal

Voice Traffic Full Buffer,

Browsing, Video Streaming Models

Ability to Simulate Packet Arrival of Actual Web Sites

Cell Site Information

Models to Simulate Fading Actual Measurement Feasible

User Placement in Cell

Indoor /Outdoor Users

Traffic Demand

Inputs

Air InterfaceSimulator

Advanced Schedulers for Radio Resource Management

Rate Predictions Using Detailed Link Curves

Application Emulation Models

3GPP/3GPP2 Application Emulation Models

Packet Tracing

RF Layer

Actual Network Configuration (Import Pathloss Information from Network Planning Tool / Actual Measurements)

Simulation

QUEST Output

Mobile Statistics Position Throughput Latency

Cell Statistics (aggregated) Throughput Power, Code Utilization for

HSPA, HSPA+

PREDICTING COVERAGE AND SIMULATING NETWORK DESIGN WITH QUEST