A Learning Based Framework for Enhancing Physical Layer Security in Cooperative D2D Network

Next-generation wireless communication networks demand high spectrum efficiency to serve the requirements of an enormous number of devices over a limited available frequency spectrum. Device-to-device (D2D) communication with spectrum reuse offers a potential solution to spectrum scarcity. On the other hand, non-orthogonal multiple access (NOMA) as a multiple-access approach has emerged as a key technology to re-use a spectrum among multiple users. A cellular users (CUs) can share their spectrum with D2D users (DUs) and in response, the D2D network can help relay the CU signal to achieve better secrecy from an eavesdropper. Power optimization is known to be a promising technique to enhance system performance in challenging communication environments. This work aimed to enhance the secrecy rate of the CUs where the D2D transmitter (DT) helps in relaying the CU's message under the amplify and forward (AF) protocol. A power optimization problem is considered under the quality of service constraints in terms of minimum rate requirements at the receivers and maximum power budgets at the transmitters. The problem is a non-convex complex optimization. A deep learning-based solution is proposed and promising results are obtained in terms of the secrecy rate of CU and the rate of D2D users.