Deep Learning Based Power Allocation for Workload Driven Full-Duplex D2D-Aided Underlaying Networks

Both Device-to-device (D2D) and full-duplex (FD) have been widely recognized as spectrum efficient techniques in the fifth-generation (5G) networks. By combining them, the FD-D2D aided underlaying networks (FN) has exhibited considerable technical advantages in terms of both spectral efficiency (SE) and energy efficiency (EE). Considering the fact that the performance of FN may be severely affected by users' workload, the workload-driven FN (WFN) must be investigated. In this paper, a deep learning based transmit power allocation (TPA) method is proposed for automatically determining the optimal transmit powers of co-spectrum cellular users (CUs) and D2D users (DUs) relying on a deep neural network. Unlike the conventional transmit-power-control schemes, in which complex optimization problems must be addressed in an iterative manner (it usually requires a relative longer computational time), the proposed scheme enables each DU to determine its transmit power with a relatively shorter time. Furthermore, an improved iterative subspace-pursuit algorithm, as the performance benchmark, is formulated for WFN. In addition, to reflect the influence imposed by the workload, the penalty-based statistical sum-date-rate (PSS) can be employed as the performance metric of WFN. Numerical results show that the proposed scheme is capable of achieving a PSS comparable with that of the traditional iterative-based algorithms even under heavy-workload scenarios, but the computational complexity of the former can be significantly reduced.