Deep Learning Meets Cognitive Radio: Predicting Future Steps

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot

Cooperative Radar and Communications Signaling: The Estimation and Information Theory Odd Couple

We investigate cooperative radar and communications signaling. While each system typically considers the other system a source of interference, by considering the radar and communications operations to be a single joint system, the performance of both systems can, under certain conditions, be improved by the existence of the other. As an initial demonstration, we focus on the radar as relay scenario and present an approach denoted multiuser detection radar (MUDR). A novel joint estimation and information theoretic bound formulation is constructed for a receiver that observes communications and radar return in the same frequency allocation. The joint performance bound is presented in terms of the communication rate and the estimation rate of the system.Comment: 6 pages, 2 figures, to be presented at 2014 IEEE Radar Conferenc