1. Wireless Performance Prediction – Rationale and Goals
March 2004
IEEE Study Group on Wireless Performance Prediction
Wireless Performance Prediction – Rationale and Goals
David G. Michelson
University of British Columbia
Department of Electrical and Computer Engineering
15 March 2004
Dave Michelson, University of British Columbia.

2. March 2004
Background
In recent years, IEEE wireless LANs have grown beyond their original role as extensions to wired LANs that provide isolated islands of wireless connectivity.
As IEEE wireless LANs are increasingly used to provide ubiquitous coverage in campus-wide environments, it has become desirable to predict their performance before access points are deployed or usage ramps up.
Current IEEE standards address the implementation of wireless devices and the operation of wireless networks, but do not address the needs of those who plan the deployment of such networks.
Dave Michelson, University of British Columbia.

3. March 2004
Background (cont.)
Our own interest in wireless performance prediction has been motivated by:
deployment of one of the world’s largest campus wireless LANs (1500+ access points covering over one million m2) at the University of British Columbia, and
the needs of our colleagues in the University Networking Program’s Wireless LAN Project
Dave Michelson, University of British Columbia.

4. Wireless System Performance Metrics
March 2004
Wireless System Performance Metrics
Which wireless system performance metrics might be of interest to designers and operators?
Ans.
Coverage
Link reliability
Throughput (as a function of traffic load)
Latency (as a function of traffic load)
PESQ* (for VoIP) (as a function of traffic load)
Effect of external interference on the above
* Perceptual Evaluation of Speech Quality
Dave Michelson, University of British Columbia.

5. The Role of Wireless Performance Prediction
March 2004
The Role of Wireless Performance Prediction
When would one want to predict wireless system performance?
Ans. - To assess the success of network design during the:
Planning Stage - when access point locations are being chosen.
Commissioning Phase - after access points have been installed, but before usage ramps up.
Maintenance Phase – after access points have been added or their locations changed, or after the environment has been changed or altered due to construction renovation, etc.
cf. Operations Phase – performance can be measured directly
but any corrective action is reactive rather than proactive .
Dave Michelson, University of British Columbia.

6. March 2004
Three Views of a Wireless Network including principal impairments and mitigation techniques
Client
Access
Point
Link Level
MAC overhead
propagation impairments
device placement
tx power settings
Cell Level
contention
QoS settings
RTS/CTS
Network Level
mutual (co-channel) and external interference
tx power settings
channel assignments
Dave Michelson, University of British Columbia.

7. Issues in Wireless Network Deployment
March 2004
Issues in Wireless Network Deployment
Limits to the performance and capacity of most modern wireless networks, including IEEE wireless LANs, are set by:
propagation impairments, – contention,
MAC overhead, – mutual and external interference.
Assuring adequate coverage and link reliability through correct access point placement is necessary but not sufficient
Wireless networks generally perform well when traffic (i.e., contention and co-channel interference) is light; the trick is to maintain and ensure good performance when traffic (i.e., contention and co-channel interference) is heavy
Dave Michelson, University of British Columbia.

8. Modeling Wireless Performance Metrics
March 2004
Modeling Wireless Performance Metrics
Our goal is to model wireless performance metrics in terms of network layout, usage, and equipment performance parameters.
These metrics and parameters can be expressed on either a point (deterministic) or area (statistical) basis.
We need to capture the complex manner in which wireless performance metrics depend upon network parameters.
Measurement-based modeling is well-suited to capturing our intuition and understanding in a form amenable to analysis and simulation.
Dave Michelson, University of British Columbia.

9. Wireless Performance Prediction using Measurement-based Models
March 2004
Wireless Performance Prediction using Measurement-based Models
Propagation
Models
Network Layout
Equipment
Performance
Models
Wireless
Performance
Metrics
Usage
Usage
Models
Equipment List
Dave Michelson, University of British Columbia.

10. Test and Measurement to Support Wireless Performance Prediction
March 2004
Test and Measurement to Support Wireless Performance Prediction
Lab tests – Equipment Performance
typically conducted in controlled environments by vendors
characterize the performance of access points and client devices in the presence of propagation impairments and interference in a deterministic manner
Field tests – Propagation and Interference
typically conducted in deployed networks by operators
characterize the propagation and interference environments in a statistical manner
Dave Michelson, University of British Columbia.

11. Propagation Modeling in Support of Wireless Performance Prediction
March 2004
Propagation Modeling in Support of Wireless Performance Prediction
Site-general models for use at the planning stage, e.g., ITU-R P.1238 (Indoor), COST-231 (Indoor), and ITU-R P.1411 (Outdoor).
Site-specific models based upon data collected by the deployed network itself at the commissioning and maintenance stages,
E.g., on command, each access point in a network emits a test signal that the other access points measure in order to construct a mutual interference matrix
Dave Michelson, University of British Columbia.

12. Why Develop WPP Recommendations and Standards?
March 2004
Why Develop WPP Recommendations and Standards?
To capture our knowledge and intuition regarding the manner in which IEEE wireless network performance depends upon physical design parameters
To provide network designers and implementers with traceable methods for comparing alternative network layouts and designs.
To stimulate development of new and better methods for wireless performance prediction by providing a benchmark against which alternative methods can be compared
Dave Michelson, University of British Columbia.

13. Task Group on Wireless Performance Prediction Proposed Purpose
March 2004
Task Group on Wireless Performance Prediction Proposed Purpose
Develop a set of models and methods for predicting wireless performance metrics, including coverage and throughput, on either a point or area basis given certain information concerning the layout, usage, and devices of an IEEE wireless LAN.
Benefit those who:
plan wireless local area networks
install wireless local area networks
maintain or upgrade wireless local area networks
develop wireless local area network planning and optimization tools
Dave Michelson, University of British Columbia.

14. Task Group on Wireless Performance Prediction Proposed Scope
March 2004
Task Group on Wireless Performance Prediction Proposed Scope
Identify: (a) scenarios in which wireless performance prediction might be performed and (b) specific wireless performance metrics useful in wireless network planning and optimization.
Identify network layout, usage, and device parameters that affect the wireless performance metrics identified in Item 1.
Develop a set of models and methods for predicting particular wireless performance metrics on either a point or area basis given the parameters identified in Item 2.
Dave Michelson, University of British Columbia.

15. Task Group on Wireless Performance Prediction Proposed Scope (cont.)
March 2004
Task Group on Wireless Performance Prediction Proposed Scope (cont.)
Specify lab measurements that will sufficiently characterize device performance (access point and clients) in the presence of propagation impairments and interference for use in Item 3.
Specify site-general usage, propagation, and interference models for use in Item 3.
Specify methods or features that vendors might incorporate into access points (and possibly client devices) in order to facilitate site-specific characterization of usage, propagation, and interference for use in Item 3.
Dave Michelson, University of British Columbia.

16. Acknowledgements The contributions of:
March 2004
Acknowledgements
The contributions of:
Shailesh Sheoran and Jamie Caplan (UNP research engineers)
Haynes Cheng, Arnel Lim, and Michael Weatherby (Engineering Physics senior project students)
are gratefully acknowledged.
Dave Michelson, University of British Columbia.