Multiantenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT
Junlin Zhang, Mingqian Liu, Yunfei Chen, Nan Zhao, Yuting Han, Ning Zhang
Abstract
Cognitive radio-enabled Internet of Things (CR-IoT) is considered as a promising technology to handle spectrum scarcity for IoT applications. Spectrum sensing enables unlicensed secondary users to exploit spectrum holes under the condition of avoiding interference with primary users in CR-IoT networks. Previous studies often assume that the noise is Gaussian while ignoring the influence of non-Gaussian noise. Moreover, multi-antenna-based spectrum sensing algorithms only consider the partial information of covariance matrix. This paper develops two multi-antenna-based spectrum sensing schemes, using fractional low-order covariance matrices to address the issue of performance degradation in impulsive noise. Specifically, the first scheme, namely, diagonal element weighting detection, exploits the diagonal element weighting of the fractional low-order covariance matrix. The latter scheme is called off-diagonal element weighting detection, which adopts the diagonal matrix weighting strategy that exploits the off-diagonal elements of fractional low-order covariance matrices. The approximate analytical expressions of the false alarm probability and detection probability are derived. These developed schemes do not employ any priori knowledge of the primary user signal. Simulation results indicate that two proposed schemes achieve acceptable performance and are robust to the characteristic exponent of the alpha-stable noise, e.g., these proposed methods could achieve a detection probability of 90% with a false alarm probability of 0.1 at GSNR = -16dB, respectively.