 but may help the reader un- derstand how these equations can be extended to CDMA traffic engineering. 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. CDMA Principles for Multicellular Systems Capacity and Quality Principles where (x, y) = demand density at (x, y) G(x, y, b) = path gain from (x, y) to antenna face b These equations give us power levels for a specified demand density (x, y). If o(x, y) represents the average density of demand, then the solutions of Mr = and TM = N still represent average power levels for reverse and forward directions. If we want a system to have 2 percent blocking, then we need the power level that satisfies the equa- tions 98 percent of the time. Solving the statistical distribution of a matrix equation is a difficult task. 11 Let us apply some loose, hand-waving mathematics to gain some insight into using these equations in traffic engineering a CDMA system. Let's make life simple and consider whether each cell sector is able to stay out of an overload condition. If noise levels are low and radio signal path gains are high, then a base station power amplifier will get close to full power only when it is nearly over- loaded. In the single-server case, that cell sector will overload when its traffic level Ob reaches the CDMA limit, a + 1. Let's make life simpler still and figure that the system overloads when a row sum or column sum of the matrix M adds up to zero or less. Let's define the sector-to-sector radio path protection in Eq. (28.30): Bbb = (28.30) for b # C The column or row sum is in Eq. (28.31): (28.31) and the system is not overloaded as long as Eq. (28.32) is satisfied. (28.32) Since Ob is a Poisson-distributed distribution and S is a weighted sum of Poisson dis- tributions, we can estimate the mean and variance 02 in Eqs. (28.33) and (28.34): (28.33) bor c (28.34) bor c If we consider the resu