Learning Frameworks for Cooperative Spectrum Sensing and Energy-Efficient Data Protection in Cognitive Radio Networks


This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs) harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU) on the channel to a fusion center (FC). We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor–critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy); then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes

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This paper was published in Directory of Open Access Journals.

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