Deep learning driven physical layer security for a simultaneously wireless information and power transfer network

Abstract

Artificial intelligent approaches have been considered as the promising techniques to enable smart communications in future wireless networks. In this paper, we investigate the deep learning based resource allocation approach for secure transmission in a simultaneously wireless information and power transfer (SWIPT) network. In particular, we design the resource allocations to maximize the minimum achievable secrecy rate of the legitimate user under the constraints of energy harvesting requirements of the energy receivers (ERs). Conventionally, the optimal or suboptimal solutions of resource allocation problems can be obtained by exploiting convex optimization approaches, which are often developed based on iterative algorithms, and always result in long computational time. To satisfy ultra low latency demands and achieve physical layer security for future SWIPT systems, we develop a DNN based approach that has the capability to optimize the power allocations for a SWIPT network, where the computational time and complexity have been significantly cut down. Numerical results are provided to illustrate that the effectiveness of our proposed DNN based approach, which is capable to achieve near optimal secrecy rate performances in comparing with convex optimization approach.