Reinforcement learning based time-domain mutual interference avoidance for automotive radar
He Xiao (Beijing University of Posts and Telecommunications)
J. Wang (TU Delft - Microwave Sensing, Signals & Systems)
Runlong Li (Beijing University of Posts and Telecommunications)
Yuan He (TU Delft - Microwave Sensing, Signals & Systems, Beijing University of Posts and Telecommunications)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Due to the extensive usage of automotive radars on vehicles, mutual interference among radars on the road is becoming considerable. To address this, we propose a time domain strategy based on deep reinforcement learning (DRL). This approach helps avoid mutual interference for automotive radars in the time domain without extra communications. The numerical simulation results demonstrate that the proposed approach can avoid interference as effectively as frequency hopping. Moreover, the time domain strategy has more advantages than frequency hopping when encountering dynamic interference.