【摘要】 认知无线电与传统无线电的最大区别在于其能够感知环境,主动去学习、适应环境。近年来,对于认知无线电的研究主要集中于多目标优化的配置决策问题。但实际的通信系统可观测到的环境参数有限,且输入输出关系复杂,需要认知无线电通过学习来理解并适应环境。针对上述问题,提出了一种基于案例推理和模拟退火思想的认知决策引擎算法。仿真结果表明,该算法具有增量自学习、多目标适用性、快速收敛等优点。更多还原

【Abstract】 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,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.更多还原