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the difference is negative). The curve in Fig. 8.7 shows the simulation result of prompt and early-late correlations. The delay is still equal to 400 chips (the scale has been changed). The next question is related to the behavior of such correlations when multipath occurs. Here the basic idea is to consider that the satellite signal is in fact duplicated

The same techniques apply, leading to similar global performance. In some speci c cases, the better synchronization available in UMTS networks allows slightly

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In the example, we are able to nd nodes at level nk + 1 to which we can assign the triplets. Now we shall prove that there are always enough nodes so that the assignment can be completed. We can perform the assignment of triplets to nodes if and only if the Kraft inequality is satis ed. We now drop the primes and deal only with the winnowed list illustrated in (14.97). We start with the in nite sum in the Kraft inequality and split it according to the output strings:

1 + e L(b1 ) e L(b2 ) P((b1 b2 ) = +1) = ln P((b1 b2 ) = 1) e L(b1 ) + e L(b2 )

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2.1.1 Fixed, Mobile and IP Convergence A key element of the seamlessness described above is the convergence of xed line and mobile phone infrastructures. There is an increasing trend towards the blending of wireline and wireless networks, especially in the core network; one research report, for example, predicts that In the core network, the boundaries between xed and mobile technologies will be largely dissolved by 2010 2012 . . . driven mainly by the universal migration to an all-IP network [17]. From the user s point of view, the xed mobile convergence will mean that the user will be able to use her services seamlessly across different locations, by switching between different networks in an uninterrupted way. Karasti et al. describe the bene ts to the user in the following terms:

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blocks, and re nement of techniques for measuring a dependent variable In this section, I describe an alternative approach to reducing error variance and minimizing the effects of nuisance variables The approach is called analysis of covariance (ANCOVA) and combines regression analysis and analysis of variance Analysis of covariance involves measuring one or more concomitant variables (also called covariates) in addition to the dependent variable The concomitant variable represents a source of variation that was not controlled in the experiment and one that is believed to affect the dependent variable Analysis of covariance enables a researcher to (a) remove that portion of the dependent-variable error variance that is predictable from a knowledge of the concomitant variable, thereby increasing power, and (b) adjust the dependent variable so that it is free of the linear effects attributable to the concomitant variable, thereby reducing bias.

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