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The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, “DLSenseNet”, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that “DLSenseNet” provides better spectrum detection than other sensing models.

The communication infrastructure between energy generation, transmission, distribution and utilization will require multi-way communications, interoperability between the advanced and existing system, and end-to-end reliable and secure communications with low-latencies. Revolutionary communication architecture is required for effective operation and control of smart grid, and cognitive radio based communication architecture can provide a unique solution. By leveraging cognitive radio technology, the suggested communications infrastructure promises to utilize potentially all available spectrum resources efficiently in the smart grid. The radio agility allows the smart grid devices to sense the unused spectrum opportunities in the surroundings and utilize them subject to interference constraints. Dynamic spectrum access enabled by cognitive radio technology can be adopted by the smart grid to exploit the underutilized frequencies in an opportunistic manner. As a result, the flexibility, efficiency, and reliability can be significantly enhanced in a cognitive radio based smart grid network.

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