A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database

The architecture of cognitive decision engine should enable fast decision making, long-term knowledge accumulating based on past operating experience, and capabilities of knowledge updating to adapt to new situations. In this paper, a hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database is proposed. Considering the user’s quality of service preferences and the wireless situations, how to determine the radio’s link parameters such as modulation type, symbol rate, and transmit power can be formulated as a multi-objective optimization problem. In the architecture, this problem is solved by using particle swarm optimization algorithms, which make cognitive radio have the fast decision-making ability when facing unknown wireless situations. The case database, which stores the past running experiences of the cognitive radio is also integrated into the proposed architecture to improve the radio’s response speed and endows the radio with the ability of learning from its previous operating experiences. Simulation results show the effectiveness of the architecture, and the proposed cognitive decision engine can dynamically and properly reconfigure the radio according to the changes in wireless environment.