Open AccessPrincipal component analysis‐based blind wideband spectrum sensing for cognitive radio
Author(s) -
Lei Kejun,
Yang Xi,
Tan Yanghong,
Peng Shengliang,
Cao Xiuying
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2016.1319
Subject(s) - cognitive radio , wideband , false alarm , detector , principal component analysis , computer science , noise (video) , independent component analysis , noise power , binary number , algorithm , channel (broadcasting) , signal to noise ratio (imaging) , component (thermodynamics) , likelihood ratio test , pattern recognition (psychology) , artificial intelligence , power (physics) , electronic engineering , mathematics , statistics , telecommunications , engineering , wireless , physics , image (mathematics) , arithmetic , quantum mechanics , thermodynamics
A principal component analysis‐based blind wideband spectrum sensing (WSS) algorithm is presented, in which the WSS issue is transformed into a sequential binary hypothesis test under the framework of the general likelihood ratio test. The proposed method operates simultaneously over all the subbands rather than one single subband each time. Furthermore, the new method overcomes the noise uncertainty problem, and can also perform well without information about the channel, the primary signal, and the noise power. Most importantly, unlike the existing classical blind wideband detectors based on the information theoretic criterion, the decision threshold for the proposed detector can be flexibly determined according to the target false‐alarm probability. Simulation results verify its effectiveness and superiority to the existing sensing algorithms.