Radar Privacy Violation in Spectrum Sharing Systems Using Neural Networks

Abstract

This paper presents the problem of interference management and radar privacy risks in shared spectrum scenarios between radar and communications systems. We propose a deep neural network that is designed and trained to reveal the location of the radar. The input of the network is a precoding matrix that a mobile terminal can use to transmit information via an uplink channel to a near-by base-station such that the amount of interference towards the radar is minimized while at the same time preserving the communications data rate above the required threshold. The results of this work suggest the need for a more complex precoder design procedures to protect the location of the radar in shared-spectrum systems. The results for detecting the radar location are compared to the available models, and we show an 92.05% improvement in detection capability in terms of the absolute error between the true radar location angle and the predicted angle, as measured with respect to the location of the mobile terminal in the communications system.