基于主用户信号谱特征的频谱感知方法

频谱感知是认知无线电的关键技术之一。首先讨论了一种基于信号频谱特征的频谱感知方法,提出了一种判决统计量,克服了噪声功率不确定性的影响。给出了算法流程,并采用ATSC（ad-vanced television systems committee）信号作为主用户信号对算法性能进行了仿真分析。结果表明：选择较多点快速Fourier变换（FFT,fast fourier transform）估计得到的理想主用户信号功率谱作为模板所得的检测概率较高;FFT点数相同情况下,增加谱模板估计时的周期图平均次数对检测性能没有多大改进。另外,不同观测时间下的仿真结果还表明增加接收信号的观测时间有利于改进算法检测性能。

Spectrum Sensing Based on Spectral Features of Primary Signals

Spectrum sensing is one of the key technologies for cognitive radios.A spectrum sensing method based on spectral features of primary signals is discussed and a new decision statistic is proposed which is not influenced by noise power uncertainty.The procedure of the algorithms is detailed and simulations are conducted to evaluate the performance of the method using ATSC(Advanced Television Systems Committee) as the primary signal.Results show that using power spectral density of primary signal obtained from larger point of fast Fourier transform(FFT) can result in higher probability of detection.When the point of FFT is kept the same,increasing number of averaging in periodogram calculation helps little to increase the detection performance.Moreover,simulation results under different observation time durations reveal that increasing the observation time can help improve the detection performance of the method.