Overview:
The recent availability of low-cost automotive radar sensors has led to their rapid use in providing environmental perception. These sensing and perception capabilities are used for enhanced car safety and enabling a variety of autonomous driving functions. Currently automotive radar deployments are low. However, with increasing market penetration, it is expected that the ad hoc operation of today’s radar sensors will lead to a significant interference problem in the future.
In this PhD project, resource allocation strategies for interference management wherein the automotive radars rely on 6G communication infrastructure are considered. Both centralized and decentralized interference management designs are expected to be developed in the project, resulting in a “radar MAC” design. Another aspect in the project is to develop interference learning methods based on physical interference signal characteristics leveraging on developments in the field of machine learning.
Your tasks:
Education and Requirements: