用于认知跳频的归一化谱双向搜索感知算法

Bidirectional search of normalized power-spectrum based sensing algorithm for cognitive frequency hopping

认知跳频被认为是消除传统跳频系统用频困扰的有效途径之一。针对认知跳频超宽带和多频隙实时频谱感知的需求,给出基于归一化谱双向搜索(bidirectional search of normalized power-spectrum,BSNP)的感知算法,BSNP以跳频频隙内的归一化功率谱作为检验统计量,通过顺序执行正向和反向搜索,感知出跳频带宽中已被占用的所有频隙。利用傅里叶变换的渐进正态性和相互独立性,可推导BSNP单次判决虚警概率的数学表达式和判决门限的闭式表达式。分析和仿真表明,BSNP可以准确地找出频带内被占用的频隙,相比于常规谱估计感知算法,可有效克服噪声不确定度对频谱感知性能的影响。

The cognitive frequency hopping technology is considered to be an effective approach to eliminate dilemma in frequency use of traditional frequency hopping systems.To fulfill technical requirements of ultra-wideband,multi-frequency-slot and real time spectrum sensing,a novel spectrum sensing algorithm based on bi-directional search of normalized power-spectrum （BSNP）used in cognitive frequency hopping is proposed.The BSNP takes the normalized spectrum of frequency slots as the detection statistics,and finds out all occupied fre-quency slots in frequency hopping bandwidth by executing forward and reverse searches in sequence.The BSNP algorithm makes use of asymptotic normality and independence of Fourier transform to derive the mathematical expressions for the decision threshold and the probabilities of false alarm of single decision.Theoretical analysis and simulation results show that the proposed algorithm can accurately identify occupied frequency slots.Com-pared to the conventional spectral estimation spectrum sensing algorithm,the BSNP algorithm can effectively o-vercome the noise uncertainty problem in spectrum sensing.