d from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright © 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website. Subscriber Traffic Modeling Modeling for CDMA work on. It makes sense to put the most effort into the areas with the most revenue opportunity. This means that our (x,y) bin database needs more information; it needs the sub- scriber demand for that location. In the second-generation (2G) cellular environment, this could be a single number for the busy-hour demand in erlangs per square kilome- ter. As we move into the third-generation (3G) world, we want to know the demand across a mix of services. Some areas will be busy voice calling regions, some will have heavy wireless data service traffic, and others will serve large volumes of multimedia message service (MMS) messages. Different services have different radio requirements and create different kinds of interference for other users. We start with demand estimates and forecasts. If we have wireless service in place, then we can augment the forecasts with measured traffic levels to estimate subscriber demand. We should remember that measured traffic levels can be truncated by system capacity and that we may need to do some reverse traffic engineering to figure out what the demand would have been to create the traffic and occupancy observed. Once we have the demand by geographic area, we divide it as well as we can into (x, y) bins for our computer models to use. This division into bins may be as simple as dividing the total demand by the total area and assigning an equal share to each bin. We recommend using a little more local knowledge than this. We know where the roads and railroads are for voice commuter traffic, we know where the parks and shopping malls are for recreational users, and we know where the major office complexes are for business users. This gives us a far more refined picture of traffic distribution. This greater level of detail will 