Open AccessRobust spectrum sensing based on spectral flatness measure
Author(s) -
Gurugopinath S.,
Muralishankar R.,
Shankar H.N.
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.2016.4712
Subject(s) - test statistic , detector , false alarm , mathematics , algorithm , robustness (evolution) , gaussian noise , white noise , monte carlo method , statistical power , fading , statistical hypothesis testing , cognitive radio , additive white gaussian noise , gaussian , statistics , computer science , physics , telecommunications , wireless , decoding methods , biochemistry , chemistry , quantum mechanics , gene
The authors investigate the spectral flatness measure (SFM)‐based spectrum sensing technique for cognitive radios. This scheme exploits the fact that under Gaussian noise, the noise‐only observations have flattened spectrum, i.e. more white, when compared with that of the observations containing the incumbent or primary signal; hence, an increased SFM under the null hypothesis. Under the null hypothesis, the authors derive the asymptotic distribution of the test statistic, and the asymptotically optimal detection threshold, with a constraint on the probability of false‐alarm. Furthermore, the authors show that this test is robust to the noise variance uncertainty (NVU) and is related to a test based on the entropy in the observed sequence. Through extensive Monte‐Carlo simulations, the authors show that the test based on SFM performs better than the existing energy detector and the blind detector, under realistic signal and fading models, all in the presence of NVU. The authors also highlight the practical utility of this technique based on experimental results.