io conditions will exist so that the promised data rates can be delivered. 47.7 The Statistical View The important trend in these models is increasing precision from increasing informa- tion. More sophisticated models are not necessarily better; rather, they resolve differ- ences that simpler models do not. We prefer to think of these models as tools for dif- ferent purposes rather than a list of models rank-ordered from inferior to superior. The statistical view starts with an average path gain for the community of sub- scribers in a cell sector, the horizontal line in Fig. 47.6. 11 The distribution of path gains in decibels has some standard deviation, 11 dB in our Advanced Mobile Phone System (AMPS) measurements. 11 We may loosely refer to the average path gain for the cell sector, the path gain for a subscriber, or the path gain for a location. In each case, we mean the radio signal path gain (not including any antenna gain) between user terminals and their serving base station antenna faces. Observations Statisical Average Distance Figure 47.6 Average path gain for a cell sector. 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. Propagation Models Modeling for CDMA Playing a little fast and loose with statistics, let us express that signal spread as a variance budget. Variance is the square of the standard deviation, so, statistically speaking, halving the standard deviation reduces our variance fourfold. Thus our ini- tial variance budget for a log-normal standard deviation of 11 dB is 121 dB² Now, the object of increasing model precision is to reduce the variance yet unex- plained by the model. We think of this as adding knowledge about the signal path. The most obvious thing we can know about the radio path is its length. In general, the fur- ther it goes, the weaker it gets. The best statistical fit to measured signal data is our inve