rminals, as described in Chap. 39. An ongoing measurement program, in concert with computer prediction, fine-tunes the mathematical models and generates a clear and correct picture of the system. We can improve the predictive process by using measurement results to refine the predictive databases. The easiest example is subscriber traffic levels. The system re- ports its usage, and we can use these reports to adjust the (x, y) bin demands. If a cell sector is consistently serving more traffic than the computer models predict, then we should increase the bin demands in that sector. It may be worth some effort to look at a map and estimate where the extra traffic is located. If there is a major highway, a large shopping area, or a big business complex, then maybe bins in that area should be getting their subscriber demand values boosted. The next step is to use measured radio signal levels to revise computer-generated propagation predictions. We drive around, we walk around, we take the elevators up Downloaded 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 Subscriber Traffic Modeling the office buildings, and we measure the radio signals. If the predicted propagation is consistently lower or higher than the measured value, then we should adjust the data- base to match reality. Our last area for comparing prediction to reality is the area of pilot pollution. As we are moving around the service area, our measurement terminals should be reporting areas where four or more pilots are competing for CDMA service. These areas should match the pilot-pollution zones on the fourth-pilot delta maps. When the measure- ments and the maps disagree, then it is worth significant effort to figure out why they are coming out differently. We should determine which propagation prediction is not matching the CDMA wireless telephone system. 48.7 Co