robots-2
Joint Radar-Communication
3260 papers • 126 benchmarks • 313 datasets
Intelligently decide how to simultaneously conduct radar and communication over a shared radio channel.
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Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication
D. Niyato, Joash Lee, Yanyu Cheng, Y. Guan, David Gonz´alez•Sat Apr 09 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 nonlearning 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.