Cyclostationarity Based Multi-Antenna Spectrum Sensing in Cognitive Radio Networks

In this paper, we propose an approach of multi-antenna spectrum sensing based on cyclostationarity for cognitive radio. The key idea of the proposed method is to extend Dandawate's generalized likelihood ratio test to take into account the estimation of all cyclic cross-correlation as well as all cyclic autocorrelation obtainable in a multi-antenna system. The proposed method is able to take advantage of spatial diversity without any prior knowledge or estimation of channel information. Furthermore, we propose a simplified method to replace the full generalized likelihood ratio test in order to reduce the computational complexity. Simulation results show the reliability of our proposed detector and demonstrate the effectiveness of using the cyclic cross-correlations, which contribute to a performance gain of approximately 2dB when a four-antenna receiver is considered.