April 2004

Roger Skidmore, WVCSlide 1

doc.: IEEE 802.11-04/441r0

Submission

Overview of Prediction of Wireless Communication Network Performance

Roger Skidmore

Wireless Valley Communications, Inc.

April 2004

Roger Skidmore, WVCSlide 2

doc.: IEEE 802.11-04/441r0

Submission

Introduction – What is “Prediction”?• Prediction means different things to different people• This presentation considers “prediction” in the context

of WLAN deployment• Important to consider that you can not deploy a WLAN

without making “predictions”– You are making a decision about what to buy, where to put

it, and how to configure it – Anything other than a purely measurement-based design (i.e.,

putting up access points, measuring, moving access points, measuring, repeat) involves some degree of prediction• Pure measurement-based design becomes very expensive, very

quickly as the size and complexity of the network increases

April 2004

Roger Skidmore, WVCSlide 3

doc.: IEEE 802.11-04/441r0

Submission

Automating Prediction with Software• Using software to model wireless network

performance is becoming more common– Goal is to minimize up-front deployment costs and

back-end network management issues– The better the design, the easier the management

• An ounce of prevention is worth a pound of cure

• Predictive algorithms and techniques refined through years of use in cellular/PCS technologies provide reliable accuracy with a re-usable methodology with regard to radio propagation

April 2004

Roger Skidmore, WVCSlide 4

doc.: IEEE 802.11-04/441r0

Submission

Typical Prediction Process

Predict Radio Wave Propagation (PHY)

Create Computer Model of Physical Environment

Create Computer Model of Equipment

Overlay RF Analysis (PHY) with Equipment / Technology Effects

(MAC)

Position / Interconnect Equipment within

Environmental Model

April 2004

Roger Skidmore, WVCSlide 5

doc.: IEEE 802.11-04/441r0

Submission

Create a Computer Model of the Environment• Manipulate various data sources (CAD files, raster

images, etc.) into a form usable for radio wave propagation prediction

• If available, structural information can dramatically improve prediction accuracy– Walls, floors/ceilings, windows, large shelving– Can usually categorize structures into broad categories

of material type• For example, Concrete, Tinted Glass, Metal Shelving, etc.

• User information can be considered– For example, user density and priority levels, traffic

patterns, service types, etc.

Example building model

April 2004

Roger Skidmore, WVCSlide 6

doc.: IEEE 802.11-04/441r0

Submission

Example Method of Modeling Equipment• Each piece of equipment is broken up into individual

modules that are linked internally• Each module defined by a component database• Modules are interconnected together to achieve final

representation of a desired piece of equipment– For example, an access point may combine a Antenna and

Transceiver modules, or may include various Cable modules

• Interconnectivity effects– For example, using different types of antennas can directly

affect the operating characteristics of devices to which they are attached

April 2004

Roger Skidmore, WVCSlide 7

doc.: IEEE 802.11-04/441r0

Submission

Create Computer Model of Equipment• Detailed description of equipment characteristics are

used to provide additional predictive results such as throughput

• Typically derived through experimental trials• Example equipment-specific parameters:

– Air interface / Protocols (e.g., 802.11a/b/g)– Antennas, Antenna Patterns– Transmit power, Data-rate specific transmit power– Hand-off thresholds– Frequency-specific effects (for multi-band equipment)– Noise / Interference rejection– Interoperability effects

April 2004

Roger Skidmore, WVCSlide 8

doc.: IEEE 802.11-04/441r0

Submission

Position / Interconnect Equipment

WLAN Access Point 204.71.202.16, Floor 4

CAT5 Cable, Floor 4

WLAN Access Point 204.71.202.16, Floor 4

CAT5 Cable, Floor 4

April 2004

Roger Skidmore, WVCSlide 9

doc.: IEEE 802.11-04/441r0

Submission

Predict Radio Wave Propagation• Well-known algorithms and methods for doing this

– ITU standards, COST-231, FCC guidelines, academic research– Industry proven to be accurate and reliable

• Well-known tradeoffs between accuracy of specific algorithms and the conditions under which the algorithms are best applied

• Algorithms vary widely in terms of accuracy, computational intensity, parameters considers, and applicability to various scenarios

• Many algorithms can be calibrated with measurement information

• Result is an accurate representation of the RF environment

April 2004

Roger Skidmore, WVCSlide 10

doc.: IEEE 802.11-04/441r0

Submission

Predict Radio Wave Propagation• Received signal strength

intensity (RSSI) on the downlink for an example 802.11g WLAN

-40 dBm

-55 dBm

-70 dBm

-85 dBm

-100 dBm

~25 feet

April 2004

Roger Skidmore, WVCSlide 11

doc.: IEEE 802.11-04/441r0

Submission

Overlay RF Analysis with Equipment / Technology Effects

• Once RF channel (PHY) environment is defined through prediction, can overlay equipment / technology-specific effects to derive operating characteristics at higher layers (e.g., throughput)

• Equipment model defines known relationship between the RF channel environment and the operating “performance” of the actual equipment

• Many algorithms can be calibrated with measurement information

• Result is more identifiable as “network performance”

April 2004

Roger Skidmore, WVCSlide 12

doc.: IEEE 802.11-04/441r0

Submission

> 1 Mbps

650 - 800 kbps500 - 650 kbps350 - 500 kbps200 - 350 kbps

800 - 1000 kbps

< 200 kbps

Active Access PointActive Access Point Overlay RF Analysis with Equipment / Technology Effects

PDA with 802.11b PCMCIA card

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