基于授权用户状态迁移统计特性的频谱感知算法

通过动态监测各个频段使用状况并机会性地接入空闲频谱,认知无线电技术可显著提高已分配频段的频谱利用效率,继而有效地缓解频谱资源日趋枯竭的现状。文章首先采用一类具有记忆特性的状态迁移模型来刻画授权用户行为;综合考虑漏检概率与虚警概率的影响,建立了频谱检测的代价函数;在此基础上,采用最大后验概率准则实现检测算法,并对理论算法进行修正,使其满足实际场景下低复杂度、低检测时延要求。借助于有限状态机模型所隐含的编码增益,该算法能显著提高检测性能,数值仿真也进一步验证了算法有效性。

Spectrum sensing in Cognitive Radio by exploiting working states characteristics of primary users

Cognitive radios （CRs） can dramatically improve spectrum utilization and hence resolving the spectrum scarcity, by monitoring the dynamic usage of licensed band aod further taking advantage of spectrums opportunities. In this paper, we suggest a general state transition model with memory to depict the states evolution process of primary users （PU）. Spectrum sensing is then formulated as the detection of a corresponding coded sequence, The maximum posterior probability （MAP） criterion is employed to perform optimum spectrum sensing, in the sensing that minimizing the detection errors. Furthermore, based on the states transition model of PU, it is modified to best serve CR applications. By fully exploring the equivalent potential coding gain, our sensing algorithm can improve the detection probability in combination with other sensing methods. The simulation results also validate the effectiveness of our method.