Autonomous decision making process supporting cognitive waveform design

In this paper, we propose a cognitive decision making process driving the dynamic reconfiguration of a radio. The solution results from an original modeling of the cognitive design task based on the definition of two scales characterizing the solution space. It exploits the predictive capabilities of evolving connectionist systems improving their reliability through incremental learning as the radio interacts with its environment. The whole algorithm has been named RALFE for reason and learn from experience based on its trial/error approach of the problem. This cognitive algorithm allows autonomous decision making with regard to multiple, possibly conflicting, operational objectives in a time-varying environment. The proposed approach is validated on a case of cognitive waveform design.