A Spectrum Sensing Scheme Based on Subspace Filtering

2017 International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Mu, Junsheng^1 ; Yang, Wei^1 ; Jing, Xiaojun^1 ; Huang, Hai^1

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing

100876, China^1

关键词: Closed form solutions;    Cognitive radio networks (CRNs);    Detection performance;    False alarm probability;    Low signal-to-noise ratio;    Noise contributions;    Spectral efficiencies;    Subspace decomposition;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/261/1/012011/pdf
DOI  :  10.1088/1757-899X/261/1/012011

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Spectrum sensing (SS) has attracted much concern of researchers due to its significant contribution on the spectral efficiency. Energy Detection (ED) has been a critical method for Spectrum Sensing in Cognitive Radio Networks (CRNS) due to its low complexity and simple implement. However, noise uncertainty in ED greatly degrades the detection performance, especially under a low Signal-to-Noise Ratio (SNR). To remove noise uncertainty as much as possible, a scheme based on subspace decomposition is proposed for SS, where the received signal is decomposed into two parts: noise subspace and signal-plus-noise subspace. Then the closed-form solution of the detection and false alarm probabilities is given on the basis of the signal-plus-noise subspace in Rayleigh fading channel. The energy of the remainders after removal of noise subspace and noise contribution in signal-plus-noise subspace is used to decide whether the primary user (PU) exists by a comparison with a redesigned threshold. Eventually, some simulations based on MATLAB platform is made to validate the proposed method.