r Koc, U.-V., J. Appl. Signal Process., 10, 1584, 2005.) In particular, the equalizers are represented with the number of taps after the name of the filtering section (e.g., FFE3-DFE2 is an equalizer with three taps in the FFE and two taps in the DFE) and with the name of the adaptive algorithm used for the estimate of the coeffi- cients. Here LMF [75] and LM8 [76] are two algorithms alternative to the LMS proposed in [74]. The effect of the equalizer in changing the decision variable distribution is clear from the figure, and the greater the equalizer complexity, the greater is the distribution change. Thus continuous optimization of the decision threshold is a key point for the optimum system working. The fact that the equalizer changes the noise distribution has also another important impact on the overall working receiver. In high-performance receivers an FEC is almost always implemented to enhance the system performance. The FEC gain in terms of SN, needed to reach a certain post-FEC Technology for Telecommunications: Integrated Optics and Microelectronics error probability is generally evaluated for a Gaussian noise at the FEC input. In case of optical systems there is a long experience that allows the gain of the most common FEC to be estimated also in the presence of the amplifier noise. However, if the error statistics changes due to an equalizer, the FEC gain can also change. This is a problem that mainly hits nonlinear equalizers and can be important in the gen- eral design of the receiver. An example of performance of the FFE-DFE is shown in Figures 5.57 and 5.58 [74]. Here the performance of a receiver in the presence of a signal carrying optical Gaussian noise (e.g., from optical amplification) is considered. In particular, the increment of the optical signal to noise ratio (ASN) needed to overcome the effect of dispersion is plotted versus the chromatic dispersion in Figure 5.57 and versus the PMD in Figure 5.58. An equalizer with nine taps in the FFE and four in the DFE is considered, with two dif