doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 1 Overview of Prediction of Wireless Communication Network Performance Roger Skidmore Wireless Valley Communications, Inc.

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 2 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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 3 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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 4 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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 5 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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 6 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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 7 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., a/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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 8 Position / Interconnect Equipment

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 9 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

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 10 Predict Radio Wave Propagation Received signal strength intensity (RSSI) on the downlink for an example g WLAN -40 dBm -55 dBm -70 dBm -85 dBm -100 dBm ~25 feet

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 11 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”

doc.: IEEE /441r0 Submission April 2004 Roger Skidmore, WVCSlide 12 Overlay RF Analysis with Equipment / Technology Effects PDA with b PCMCIA card Best Server