Primary User Signal Detection Based on Virtual Multiple Antennas for Cognitive Radio Networks

Primary User (PU) signal detection is critical for cognitive radio networks as it allows a secondary user to find spectrum holes for opportunistic reuse. Eigenvalue based detection has many advantages, such as it does not require knowledge on primary user signal or noise power level. However, most of the work on eigenvalue based detection methods presented in the literature require multiple sensing nodes or receiving antennas so that they cannot be directly applied to single antenna systems. In this paper, an effective eigenvalue based PU signal detection method is proposed for a cognitive user equipped with a single receiving antenna. The proposed method utilizes the temporal smoothing technique to form a virtual multi-antenna structure. The maximum and minimum eigenvalues of the covariance matrix obtained by the virtual multi-antenna structure are used to detect PU signal. Compared with the previous work, the presented method offers a number of advantages over other recently proposed algorithms. Firstly, the presented approach makes use of power method to calculate the maximum and minimum eigenvalues, it has lower computational complexity since the eigenvalue decomposition processing is avoided. Secondly, it can reduce system overhead since single antenna is used instead of multiple antennas or sensing nodes. Finally, simulation results show that performance of the proposed method is close to that of maximum-minimum eigenvalue detection using multiple antennas. Received 18 June 2013, Accepted 10 August 2013, Scheduled 14 August 2013 * Corresponding author: Fulai Liu ([email protected]). 214 Liu, Guo, and Sun