ther than sampling them. We can use the computer to integrate the small numbers to come up with precise estimates based on the propa- gation and traffic data. Let us go back to our coin-flipping example and ask about the probability of flipping a fair coin and getting 10 heads in a row. We could flip the coin hundreds of thousands of times to get a pretty good estimate, or we could simply do the arithmetic of taking one-half to the tenth power. The probability of 10 heads in a row is 1/1024, and it is a lot easier to do the analysis than to simulate all those events with all those small proba- bilities. An analytical tool uses the same propagation and demand data for each bin, but in- stead of simulating calls, it adds up the statistical properties of each bin for each base station. We can develop mathematical calculations to integrate the CDMA equations over all the bins in the system for all the base stations and for all the services. The re- sult is a precise estimate of blocked calls and poor radio performance for each bin and for each service. This approach is particularly advantageous for the low-density areas of the system. If our aim is global coverage of an entire geographic service area, then an analytical tool can do its calculations for every bin, including bins where the subscriber traffic level is very low. A simulation is a better tool for finding out message rates, handoff rates, and gross event counts. It deals directly with the dynamics of subscriber activity, placing calls, moving from bin to bin, and ending calls. The analytical approach is better for finding 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 Modeling for CDMA small probabilities and ensuring QoS over a geographic area and not just for a popula- tion of subscribers. 48.5 Pilot Delta Maps For CDMA capacity planning, the CDMA equati