摘要
针对已有的双门限特征值频谱感知算法存在忽略本地感知用户可靠性差异及融合判决方式开销大的缺点,提出了一种基于可信度的双门限DMM协作频谱感知算法(DT-CDMM),用于进一步提升协作感知性能。所提算法在最大最小特征值差(DMM)算法的基础上,建立了基于特征极限分布的双门限DMM算法作为本地感知,采用触发式的软、硬判决相结合的判决机制来减少系统开销,以本地感知性能与可信度加权的方式得到全局判决结果,并对硬判决进行自适应补偿。仿真结果表明,较已有的双门限特征值算法以及双门限能量检测算法,DT-CDMM算法在噪声不确定的环境下提升了多用户协作检测的概率。
Because the eigenvalue-based double thresholds spectrum sensing algorithms overlook the reliability difference between second users(SU)and the high expense of fusion decisions,a double thresholds DMM cooperative spectrum sensing algorithm based on credibility(DT-CDMM)was proposed to improve the sensing performance.Based on the difference between maximum and minimum eigenvalue(DMM)algorithm,a double thresholds DMM spectrum sensing algorithm based on limiting eigenvalue distribution is used as SU’s local sensing,a triggered mechanism combined with soft and hard decisions is established to cut the system expenses,the final decision is obtained via the weighting of SU’s sensing performance and local credibility,and a self-adaption compensation for hard decisions is applied.Theory analysis and simulations show that the DT-CDMM improves the probability of multi-user collaborative detection compared with double eigenvalue thresholds algorithms and double thresholds energy detection algorithm when noise is undefined.
作者
高鹏
刘芸江
高维廷
李曼
陈娟
GAO Peng;LIU Yun-jiang;GAO Wei-ting;LI Man;CHEN Juan(Information and Navigation Institute,Air Force Engineering University,Xi’an 710077,China;Computer Institute,Xi’an Aeronautical University,Xi’an 710077,China;The Army of 91917,Beijing 102401,China)
出处
《计算机科学》
CSCD
北大核心
2018年第9期166-170,182,共6页
Computer Science
基金
国家自然科学基金(61571364)
博士后科学基金(2016M603044)资助
