摘要: |
The project team considers the wireless scheduling problem of jointly scheduling resources (activating/de-activating and communications/sensing mode selection) at various locations on a vehicle, in order to optimize for both communications and sensing (e.g. through in-band radar). The resources could be antenna resources located at various points on a car (e.g. sides, back-bumper, front). An example scenario would be a car simultaneously engaged in vehicle-to-vehicle (V2V) communications (with another car), vehicle-to-infrastructure (V2I) communications with a base-station, and sensing the environment (for location, obstacles, etc.). The resource allocation task here would be to dynamically select each of the resources for communications or sensing. When considering infrastructure nodes, one could also consider turning off resources to save energy. The project team builds on their earlier research to study a few important new directions:
(1) Dynamic scheduling algorithms that use queue-lengths, channel state and current sensed-state in order to result in smaller delays and improved sensing fidelity. These algorithms would dynamically move modalities (communications/radar) across multiple antenna resources (e.g. different arrays located on multiple locations on a car) to optimize for both communications and sensing.
(2) Online-learning-based algorithms to optimize the use of various antenna resources (e.g. learning-based beam pattern optimization for improved sensing). |