A learning method for cognitive engine based on CBR and Simulated Annealing
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, multiobjective adaptation and rapid convergence.
cognitive engine artificial intelligence casebasedreasoning Simulated Annealing cognitive radio
Liu Yi-jing Li Ying Wei Sheng-qun
Department of wireless communication, Institute of Communication Engineering, PLAUST, Nanjing, China China Electronics Equipment System Engineering Corporation, Beijing, China
国际会议
哈尔滨
英文
503-507
2011-01-18（万方平台首次上网日期，不代表论文的发表时间）