Open AccessEigenvalue ratio based blind spectrum sensing algorithm for multiband cognitive radios with relatively small samples
Author(s) -
Yang Xi,
Lei Kejun,
Hu Li,
Cao Xiuying,
Huang Xiaoyu
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.1658
Subject(s) - cognitive radio , eigenvalues and eigenvectors , algorithm , statistic , spectrum (functional analysis) , principal component analysis , mathematics , test statistic , random matrix , noise (video) , channel (broadcasting) , signal to noise ratio (imaging) , computer science , statistics , statistical hypothesis testing , wireless , artificial intelligence , telecommunications , physics , image (mathematics) , quantum mechanics
The multiband detection problem in relatively small sample scenarios where the number of subbands is comparable to the number of samples in magnitude is described. Combined with the sequential hypothesis testing, an eigenvalue ratio based method is proposed for the multiband spectrum sensing (MSS). As the distribution of the new statistic when only noise is present can be precisely obtained by using the random matrix theory, the proposed method is able to reliably set the theoretical threshold, and outperforms the traditional MSS methods based on information theoretic criteria and principal component analysis, especially in the cases with small samples. Meanwhile, the proposed method belongs to a blind detection scheme and can be used in cases without prior knowledge of the primary signal, the channel and the noise. Simulation results show the superiority of the proposed method.