认知无线电空闲频谱的联合检测算法

为了提高认知无线电中感知用户对主用户(弱信号)的检测性能,提出了一种多个感知用户合作的分布式优化联合检测算法.该算法在知道主用户信号和噪声的概率分布的条件下,通过最优化理论中的逐步二次规划法,联合求解检测系统的最优融合准则和各感知用户的最优判决门限,使系统联合检测概率取极大值.该算法不受接收机检测方式的限制,数值求解收敛速度快.仿真结果表明,感知用户为5个、融合中心虚警概率为0.1、相关符号累积为255次、不同融合准则下各感知用户信噪比相同时,与最好的固定融合准则检测算法相比,该算法使联合检测概率在高斯白噪声信道和平坦瑞利衰落信道中分别提高了13%和5%.

Collaborative Detection Algorithm of Idle Spectrum in Cognitive Radio

To improve the detection performance of sensing users for primary users(weak signals) in the cognitive radio, put forward the optimal collaborative detection algorithm by many sensing users. In the collaborative detection process, the decision of each sensing user is independent. Through optimization theory analysis and simulation, when the false alarm probability of the fusion center is constant, detection performance contrast curve and the contrast curve of detection probability changes with signal to noise radio ( )also indicate that compared with the fixed fusion rules detection algorithm, optimal collaborative detection algorithm has better performance. The results also show in the collaborative detection algorithm, when attendees grow from one to five, as a new attendee joins, the detection performance of primary user can be improved 1~2dB. Moreover, the performance of optimal collaborative detection algorithm has obvious performance improvement as the number of accumulated symbols grows.