A Self-Learning Method for Cognitive Engine Based on CBR and Simulated Annealing
Yi Jing Liu, Li Ya Chai, Jing Min Liu, Bo Wen Li · Advanced materials research · 2012
The essential difference of cognitive radio from traditional radio lies in its ability to sense, learn and adapt to the environment. Recently, the research for cognitive radio has focused on the configuration problems of multi-objective optimization. However, in actual communication systems, the observable environment parameters are limited. Besides, the relationship between the system’s inputs and outputs is often complicated. Thus, Cognitive radio (CR) needs to understand and adapt to the environment through learning. To solve the problem mentioned above, a self-learning method for Cognitive radio decision engine based on CBR and Simulated Annealing is proposed. The simulation results show that the proposed method has the advantages of self-learning, multi-objective adaptation and rapid convergence.