Cooperative Jarque-Bera Statistic Based Spectrum Sensing Using MIMO Decision Fusion

Cognitive Radio (CR) is a novel promising approach proposed to ensure efficient spectrum utilization. It is the obvious solution to the spectral congestion problem. Spectrum sensing is the most essential functionality required for the practical implementation of cognitive radio networks. An efficient spectrum sensing method must detect very weak primary user signals while being sufficiently fast and low cost to implement. In this paper, the Jarque - Bera (JB) statistic based spectrum sensing is studied. It is a robust sensing method that has been found to outperform all the conventional methods. In this paper, this method is extended to a cooperative scenario with Multiple Input Multiple Output (MIMO) decision fusion. This paper specifically addresses the issue of deep fading in the channel between the CR users and the Fusion Center (FC), the Common Control Channel (CCC), by providing multiple antennas at the FC. Three channel-aware binary-decision fusion rules are presented and the simulation results show that the proposed technique improves the efficiency and reliability of the JB sensing method under worst channel conditions. Also, the effect of increasing the number of antennas at the FC is analyzed.