AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing

TL;DR

This paper presents a deep reinforcement learning-based framework for autonomous vehicles to adaptively optimize integrated communication and sensing waveforms, significantly enhancing performance in dynamic environments.

Contribution

It introduces a novel Markov decision process and deep reinforcement learning approach for adaptive waveform optimization in integrated communication and sensing for autonomous vehicles.

Findings

Achieves up to 46.26% performance improvement over baseline methods.

Effectively adapts to dynamic and uncertain radio environments.

Enhances joint communication and sensing capabilities in autonomous vehicles.

Abstract

Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task's performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS's waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment's dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.