A DNN Framework for Secure Transmissions in UAV-Relaying Networks with a Jamming Receiver

Abstract

Unmanned aerial vehicle (UAV) communication is one of the enabling technology in the 5G/B5G era, which has received extensive research attention recently. In this paper, we consider a UAV employing amplify-and-forward protocol to facilitate the secure transmissions for a legitimate user pair, where the direct link is not available. In order to protect the legitimate transmissions and prevent the eavesdropper from intercepting the source message, the legitimate receiver launches a jamming signal to confuse the eavesdropper. In this regard, the optimal jamming power and UAV placement are jointly investigated towards the highest secrecy rate. For this problem, we first propose an efficient power control scheme based on bi-section search with fixed UAV placement. Then, a deep neural network (DNN) is constructed and trained based on the data from exhaustive search in order to determine the best UAV placement. Through numerical simulations, we verify that the trained DNN effectively approximates the optimal UAV placement, also demonstrated is the evident superiority of our proposed scheme as compared to the baselines in terms of security performance.