lting distribution of weighted traffic and CDMA resource used to be a normal bell-shaped distribution, then the condition for 2 percent blocking is 20 < a.12 11It is hard enough when the determinant of the matrix M remains far from zero. Unfortunately for us, the CDMA equations are interesting precisely when the determinant of the matrix M is close to zero because that is when the CDMA carrier is close to its saturation level. 12If we are using more exact numbers from a statistical table, then the condition for 1 percent blocking is + 2.33o < a and the condition for 2 percent blocking is + 2.060 < a. 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 CDMA Principles for Multicellular Systems This approach may have a lot of approximations, but it gives a good sense of CDMA capacity for small-cell, interference-limited environments. The values are the in- terference contributions from one cell sector to another. Taking this cross-interference into account, we find the average traffic and the standard deviation o and make sure that the + 20 < a for 2 percent blocking. When multiple channel sizes are involved, as with a mix of voice and data service, there is some scaling and adding involved, but the statistical model is basically the same. Let us define mean and standard deviation o for the combined system in Eqs. (28.35) and (28.36): Udata (28.35) avoice Adata (28.36) We estimate that we have 2 percent blocking when u + 2o = 1. 28.6 Conclusion In this chapter we have introduced the application of the CDMA equations to estima- tion of cell capacity and to traffic engineering for the air interface. We have attempted to apply this to an interference-limited environment, that is, to a multiple-cell system serving many customers and, at times, operating near or at capacity. Understanding what happens to the CD