oes not de- pend on the the information (the data bits) that is transmitted or received. In chapter 3 and 4, it will be shown how linearity can be exploited to significantly facilitate analysis of up- and downconversion behavior. However, in almost any real system, it also occurs that two information-carrying (data) signals (t) and uz(t) intermodulate. This happens when the system's output depends, for instance, on the product 1 (t) X (t) of the two data signals. The intermodulation of two data signals is truly nonlinear: the principle of superposition no longer holds. Fig. 2.6 illustrates the difference between the linear intermodulation of a data signal 3 With regard to PAs, it is possible to handle them using the HTM-formalism in chapter 3 as well as the separation of time constants methods in chapter 5. 2.2 GOOD MODELS FOR TELECOMMUNICATION FRONTENDS: ARCHITECTURES AND THEIR BEHAVIORAL PROPERTIES S(f-f) U 1 (f) U2(f) Figure 2.6: Difference between linear and nonlinear intermodulation. (a) Linear intermodulation involves the product of a (a priori unknown) data signal u(t) and a (known) carrier. As a result, the information contained in u(t) is shifted along the frequency axis. (b) Nonlinear intermodulation involves the product of two data signals 1 (t) and u2(t). As a result, 1 (t) is upconverted and, on top of that, its content is spread out over the frequency axis. Typically, this spectral spreading is not desired. and a known carrier and the nonlinear intermodulation of two data signals. Nonlinear intermodulation is in most cases undesired. Too large a contribution to the overall signal content may very well ruin the system's performance. In order to prevent this from happening, a good design should keep the signal components due to nonlinear intermodulation as small as possible. This means that their magnitude is well below that of the signal components generated by linear system behavior. Good design there- fore requires most telecom building blocks to behave almost linearly. In traditional literature [W