Robust spectrum sensing based on spectral flatness measure

Abstract

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.

Non‐orthogonal multiple access‐aided MIMO cognitive radio networks with selective reporting

“…We now describe the first setup. The experimental setup is as in References 66 and 67 is described as follows. The PU operates at a center frequency 2.48 GHz with a bandwidth of 1 MHz.…”

Geometric power detector for spectrum sensing under symmetric alpha stable noise

“…We now study the performance of GP detector on the data obtained from an experimental setup similar to [1], described as follows. The centre frequency of the primary user is set at 2.48 GHz.…”

“…Introduction: Goodness-of-fit tests (GoFTs) (see [1] and references therein) for spectrum sensing (SS) [2] in cognitive radios (CRs) have received considerable attention over the past decade due to their simple construction and being blind to the knowledge of primary-only and fading statistics. The test statistic and the detection threshold in a GoFT depend exclusively on the noise-only observations.…”

“…Clearly, the GP detector offers the best performance. number of observations M probability of detection GP FLOS p = 0.7 FLOS p = 0.5 FLOS p = 0.3 FLOS p = 0.1 FLOS p = 0.01 Experimental results: We now study the performance of GP detector on the data obtained from an experimental setup similar to [1], described as follows. The centre frequency of the primary user is set at 2.48 GHz.…”

Supervised Learning Spectrum Sensing Method via Geometric Power Feature

“…Firstly, the GP extracted from the observed signal is used as a feature vector, and then it is trained and tested using supervised learning; finally, this method is compared with the supervised learning spectrum sensing method based on ES and DE features. Due to the excessive number of supervised learning methods, this article selects SVM and KNN for simulation experiments using the actual captured data set [31]. Simulation results show that the proposed method has the best perceptual performance, especially at low SNR.…”