Decision making algorithms for cognitive radio and optimization of the reconfigurability mapping for the numerical architecture of implementation
In this thesis we focus on the development of a decision making method for the cognitive radio receiver that dynamically adapts to its environment. The approach that we use is based on the statistical modeling of the radio environment. By statistically characterizing the observations provided by the radio sensor, we set up statistical decision rules that take into account the observations’ errors. This helps to minimize the rate of bad decisions. Also, we aim to use the intelligent capacities to reduce the computational complexity in the receiver chain. Indeed, we identify decision scenarios that limit some operators. In particular, we address two decision scenarios that adapt the presence of the equalization and of the beamforming to the environment. The limitation of these two operations helps to reduce the computational complexity in reception. Finally, we integrate our decision method and the two decision scenarios in a management architecture of reconfiguration and intelligence.