认知无线电中基于可信度的感知节点集选择

Selection of Sensing Node Set in Cognitive Radio Based on Degree of Confidence

联合谱感知虽然可提高系统的感知性能,但随着感知节点数目的增加,系统资源的占用越来越多,系统传输效率下降.该文详细分析了联合谱感知方法的感知性能,得到感知节点集感知性能与感知节点数目和平均接收信噪比之间的关系,在此基础上给出一种感知节点集的选择方法.针对认知网络中可能存在的不可信节点对感知节点集选择方法的影响,提出可有效剔除不可信节点的可信度检测方法.综合上面两种方法,提出了基于可信度的感知节点集选择算法,在进行感知节点集选择的同时可有效剔除不可信节点,有效地保证了所选节点集的感知性能.仿真结果验证了算法的有效性和可靠性.

Although collaborative spectrum sensing can greatly improve sensing performance, transmission efficiency will decrease with the number of sensing nodes that consume more system resources. The sensing performance of collaborative spectrum sensing is investigated, and relationship among the performance, node number and average signal-to-noise ratio derived. Based on the study, a method for selecting a set of sensing nodes is proposed. A method for determination of the confidence degree is presented to eliminate trustless nodes in the cognitive radio （CR） network, which adds negative effect to the selection method. Using the above two methods, an algorithm for selecting sensing node set based on confidence is proposed, which can effectively eliminate trustless nodes in the CR network and guarantee the sensing performance of the set. Simulation results show validity and reliability of the algorithm.