rse-fourth-power rule shown in Fig. 47.7. The residual standard deviation of path gain, once distance is removed, is about 8 dB. We can tell ourselves that about half the variability of wireless radio path gain is "explained" by distance, leaving about 64 dB². We can increase our knowledge about the path by finding out the terrain path (see Fig. 47.5) and using that information in a more refined model. In the vehicular world, this removed about half the remaining variability, so the standard deviation came down to about 6 dB, with variance about 36 dB² Knowing about streets and rooftops might bring the variability down to 4 or 5 dB (about 20 dB² with tools such as the Walfisch-Ikegami model. The more we know about the base- station-to-user-terminal signal path, the more precisely we can estimate its path gain. The fundamental limit of this process is not zero. Rayleigh fading varies the radio path on a microscopic scale not within the scope of these models. This Rayleigh varia- tion, while not log-normal, has a standard deviation of about 3 dB, SO there is a vari- ance 10 dB2 2 in a narrowband channel that propagation models will not explain. Spread-spectrum systems have less variation due to fading, depending on the delay spread, as discussed in Sec. 2.1.4. Observation Best Fit Curve Distance Figure 47.7 Path gain as a function of distance. 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 Propagation Models The portable telephones that dominate today's wireless market have more serious is- sues and more sophisticated solutions. The variance budget with subscribers going from parking basements to high-rise office buildings is considerably higher than the AMPS 121 dB² To help us, we have in-building models that reduce this higher vari- ance and get better predictions of path gain. As we move from macrocells and microcells to