Additional information is available at the end of the chapter http://dx.doi.org/10.5772/46470
IntroductionAdvances in electronics and telecommunications are leading to complex systems able to efficiently use the available resources. Fast electronics, complex modulation schemes and correction codes enable transmissions on channels with unfavorable characteristics, coexistence between different services in the same frequency bands and high transmission rates. However, the complexity of such communications systems often prevents analytical characterizations. For example, figures of merit such as the Bit Error Rate (BER) are difficult to determine analytically for transmission schemes involving correction codes and communication channels with Inter-Symbol Interference (ISI) and fading. In such cases, the system is characterized through Monte Carlo simulations [1,2]. The Monte Carlo framework involves simulations of the whole system under analysis. For example, when considering a communications system, the whole transmission-reception chain is simulated. A large number of sequences are sent through the simulated system and the message recovered by the simulated receiver is compared to the original transmitted sequence. This comparison allows one to determine the average number of transmission errors and compute the BER.Monte Carlo simulations can be applied to almost any system although their implementation and computation requirements can be significantly high. In addition to this, precision problems can arise when the quantity to be estimated is significantly low. For example, BERs of the order of 10 −8 − 10 −9 require at least 10 9 − 10 10 simulation runs. Conversely, analytical models have a limited applicability and usually adopt approximations (i.e., model linearization) that can yield a poor description of the system under analysis.In order to overcome the limitation of Monte Carlo and analytical techniques, semi-analytic approaches have been previously implemented [1,2]. In a semi-analytic framework, the knowledge of the system under analysis is exploited to reduce the computational load and complexity that full Monte Carlo simulations would require. In this way, the strengths of both analytical and Monte Carlo methods are effectively combined. Semi-analytic techniques are a powerful tool for the analysis of complex systems.