多集采样和非线性最小二乘估计的频谱感知

Spectrum sensing using multicoset sampling andnonlinear least squares estimation

针对认知无线电和网络中可用频带的感知,提出了一种基于多集采样和非线性最小二乘估计的宽带频谱感知方法.首先,基于多集采样器,把可用的频带划分为有限数量的频道,得到一种基于采样参数和噪声功率的检测阈值的理论表达式,并计算出采样数据的相关矩阵;然后,采用一个非线性最小二乘估计器来估计被占用的频道和空闲频道,同时,采用顺序前向选择算法来降低估计器实现的复杂性;仿真实验结果表明,相比于传统的基于能量检测和其他常用的频谱感知方法,新方案不仅在采样率方面有可观的节省,而且在相同的信噪比(SNR)下能获得更好的检测概率和虚警概率。

A wideband spectrum sensing method based on multicoset sampling and nonlinear least squares estimation is proposed for the sensing of available frequency bands in cognitive radio and networks. Firstly, basing on the multioset sampler, the available frequency bands is divided into a finite number of spectral bands and a theoretical expression was derived for detection threshold based on the sampling parameters and noise power,and the correlation matrix of the sampled data was calculated. Then, a nonlinear least squares estimator was used to estimate the occupied channels and vacant channels and a sequential forward selection algorithm was used to reduce the complexity of the estimator implementation. The simulation results show that, compared with the traditional spectrum sensing method based on energy detection and other commonly used spectrum sensing method, the novel scheme has considerable savings in terms of sampling rate, better detection probability and false alarm probability under the same signaltonoist ratio(SNR).