AI-Based Secure NOMA and Cognitive Radio enabled Green Communications: Channel State Information and Battery Value Uncertainties

In this paper, the security-aware robust resource allocation in energy harvesting cognitive radio networks isconsidered with cooperation between two transmitters while there are uncertainties in channel gains and batteryenergy value. To be specific, the primary access point harvests energy from the green resource and uses timeswitching protocol to send the energy and data towards the secondary access point (SAP). Using power-domainnon-orthogonal multiple access technique, the SAP helps the primary network to improve the security of datatransmission by using the frequency band of the primary network. In this regard, we introduce the problem ofmaximizing the proportional-fair energy efficiency (PFEE) considering uncertainty in the channel gains and batteryenergy value subject to the practical constraints. Moreover, the channel gain of the eavesdropper is assumed to beunknown. Employing the decentralized partially observable Markov decision process, we investigate the solutionof the corresponding resource allocation problem. We exploit multi-agent with single reward deep deterministicpolicy gradient (MASRDDPG) and recurrent deterministic policy gradient (RDPG) methods. These methods arecompared with the state-of-the-art ones like multi-agent and single-agent DDPG. Simulation results show that bothMASRDDPG and RDPG methods, outperform the state-of-the-art methods by providing more PFEE to the network.