A Beamforming Approach to Smart Grid Systems Based on Cloud Cognitive Radio

In this paper, we desire to use cognitive radio (CR) channels for communication among a wireless network of smart meters. However, self-interference critically limits the performance of CR systems. This is due to the coexistence of many unplanned systems simultaneously accessing the same signaling bands in an uncoordinated manner. To solve this problem, we show a beamforming approach that effectively mitigates the self-interference effects of the smart meter channel. The beamforming approach is based on minimum mean squared error (MMSE) method in smart meter systems. The MMSE beamformer usually requires accurate channel estimates and noise-plus-interference power estimates for effective mitigation of self-interference in CR systems. In this paper, we propose novel channel estimation and noise-plus-interference power estimation methodologies that efficiently exploit the preamble feature of the IEEE802.22 wireless regional area network (WRAN). Our framework is premised upon the utilization of a cloud computing smart grid infrastructure that hosts the IEEE 802.22 WRAN CR standard. The simulation results for a smart grid system with the MMSE beamformer illustrate significant improvements in system capacity and BER.