Indoor radio propagation modeling for system performance prediction

This thesis aims at proposing all the possible enhancements for the MultiResolution Frequency-Domain ParFlow (MR-FDPF) model. As a deterministic radio propagation model, the MR-FDPF model possesses the property of a high level of accuracy, but it also suffers from some common limitations of deterministic models. For instance, realistic radio channels are not deterministic but a kind of random processes due to, e.g. moving people or moving objects, thus they can not be completely described by a purely deterministic model. In this thesis, a semi-deterministic model is proposed based on the deterministic MR-FDPF model which introduces a stochastic part to take into account the randomness of realistic radio channels. The deterministic part of the semi-deterministic model is the mean path loss, and the stochastic part comes from the shadow fading and the small scale fading. Besides, many radio propagation simulators provide only the mean power predictions. However, only mean power is not enough to fully describe the behavior of radio channels. It has been shown that fading has also an important impact on the radio system performance. Thus, a fine radio propagation simulator should also be able to provide the fading information, and then an accurate Bit Error Rate (BER) prediction can be achieved. In this thesis, the fading information is extracted based on the MR-FDPF model and then a realistic BER is predicted. Finally, the realistic prediction of the BER allows the implementation of the adaptive modulation scheme. This has been done in the thesis for three systems, the Single-Input SingleOutput (SISO) systems, the Maximum Ratio Combining (MRC) diversity systems and the wideband Orthogonal Frequency-Division Multiplexing (OFDM) systems.