基于多模型假设检验的认知无线电频谱感知方法

提出一种基于序贯概率似然比多模型假设检验的认知无线电协作频谱感知方法,用于检测可能含有不同结构和参数不确定性的未知信号.传统的认知无线电协作频谱感知方法(如基于序贯概率似然比的单模型假设检验、M元假设检验等),仅限于处理已知信号分布,不考虑信号分布的不确定性,可能会造成检测误判.所提出方法不仅可以处理认知无线电信号分布模型的不确定性问题,而且可以得到满足错误概率约束的有效检测.对频谱感知的一个典型场景进行仿真实验,结果表明所提出基于序贯概率似然比多模型假设检验方法相对于传统方法的检测有效性.

Cognitive radio spectrum sensing approach based on multiple-model hypothesis testing

This paper presents a multiple-model hypothesis testing approach based on sequential probability ratio test for cognitive radio spectrum sensing to detect unknown signal that may have multiple possible distributions with different structural or parametric uncertainties. The traditional cognitive radio spectrum sensing scheme (e.g., single model hypothesis testing based on sequential probability ratio test and M-ary hypothesis testing) may be not correct, because it only handles the totally known signal distribution case without considering the uncertainties of signals. The proposed multiple-model hypothesis testing scheme not only copes with the uncertainties of signals, but also has a setting that can provide efficient detection results. Performance of the proposed scheme is evaluated for spectrum sensing in an illustrative scenario. Simulation results demonstrate its detection efficiency compared with the traditional schemes.