Learning engine for cognitive radio based on the immune principle

A learning engine for cognitive radio based on the immune principle is proposed, in which the monkey-king´s marrying mechanism is introduced to improve the learning efficiency, and the dissimilar matrix as well as fuzzy selection are utilized to simplify computation and to accelerate the learning speed. The framework of the proposed learning engine has been given. The learning engine has been implemented in MATLAB, and a test bed that simulates the wireless communication system has been developed using SIMULINK based on the 802.11a model. Simulations have been done on the parameters adjustment of multi-carrier system to perform multi-objective optimization. Experimental results indicate the superior performance of our learning engine over the genetic algorithm-based one. Furthermore, the learning engine also has the potential of decision-making because it has combined the theories of knowledge base and case-based decision-making owing to the recollection of immune system.