doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 1 Wireless Performance Prediction – Proposed Framework Presented by David G. Michelson University of British Columbia 10 March 2004

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 2 Background Our interest in wireless performance prediction has been motivated by: –deployment of one of the world’s largest campus wireless LANs (1500+ access points covering million sq. m.) at the University of British Columbia and –the needs of our colleagues in the University Networking Program

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 3 Wireless Performance Ans. Coverage Link reliability Throughput (as a function of traffic load) Latency (as a function of traffic load) Which wireless performance parameters are of most interest to designers and operators? One of our goals is to model these performance parameters in terms of network layout, usage, and equipment performance parameters. Note that these parameters can be expressed on either a point (deterministic) or area (statistical) basis.

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 4 The Role of Wireless Performance Prediction When would one want to predict wireless system performance? Ans. 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.

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 5 Issues Propagation impairments, MAC overhead, contention, and mutual interference set the ultimate limits to the performance and capacity of most modern wireless networks, including IEEE wireless LANs Assuring adequate coverage and link reliability through correct access point placement is a necessary first step Wireless networks generally perform well when traffic is light; the trick is to maintain and ensure good performance when traffic is heavy

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 6 Client Access Point Link Level propagation impairments MAC overhead Cell Level contention QoS settings RTS/CTS Network Level mutual (co-channel) interference power settings channel assignments Three Views of a Wireless Network including principal impairments and mitigation techniques

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 7 Testing to Support Wireless Performance Prediction Lab tests (in controlled environments by vendors) to characterize the performance of access points and client cards in the presence of propagation impairments and interference Field tests (in deployed networks by operators) to characterize the propagation and interference environments –methods and models based upon generic results, e.g., ITU-R P.1238 (Indoor), COST-231 (Indoor), and ITU-R P.1411 (Outdoor) for use at the planning stage –methods and 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

doc.: IEEE /248r0 Submission March 2004 Dave Michelson, University of British Columbia.Slide 8 Possible Outcomes of WPP Standards Efforts 1.A list of essential wireless performance parameters 2.A list of scenarios in which WPP might be performed 3.A set of models and methods for predicting particular wireless performance parameters on either a point or area basis given certain network layout, usage, and equipment parameters or models 4.Specified lab tests that sufficiently characterize access point and client performance in the presence of propagation impairments and interference for use in (3). 5.Site-general usage, propagation, and interference models for use in (3). 6.Methods or features that vendors might incorporate into access points (and possibly client cards) to facilitate site-specific characterization of usage, propagation, and interference for use in (3)