DNN-Based Resource Allocation for Cooperative CR Networks with Energy Harvesting

Cognitive radio (CR) and energy harvesting (EH) have been deemed two promising technologies in the spectrum-scarce and energy-limited wireless networks. In this paper, the cooperative cognitive radio network (CRN) with EH is considered, where a secondary user (SU) close to the secondary base station (SBS) employs power splitting for EH and assists to relay the data for another SU far away from the SBS. A SU sum-rate maximization problem is formulated under the constraints of the power budget at the SBS, the interference threshold of the primary network, and SU QoS. To tackle this problem, a resource allocation algorithm based on an improved deep neural network (DNN) is proposed. In order to accelerate the convergence of the DNN loss function, transfer learning is exploited to initialize the DNN weights. The loss between the DNN output and the optimal transmit power obtained by the conventional solution is stored in the memory pool, where the samples with large losses are used to train the DNN. Simulation results show the efficiency of our proposed DNN-based resource allocation scheme, which outperforms the normal DNN-based resource allocation and conventional resource allocation scheme in terms of the computation time.