Deep reinforcement learning for time allocation and directional transmission in joint radar-communication - Lee, Joash (2022)
Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to non-learning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.