用于集成通信和传感的自主车辆的AI支持的毫米波配置
AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing
摘要：
集成通信和传感（ICS）最近已经成为无处不在的传感和物联网应用的一种使能技术。对于ICS应用于自动驾驶汽车（AVs），优化波形结构是最具挑战性的任务之一，由于传感和数据通信功能之间的强烈影响。具体地，数据通信帧的前同步码通常用于感测功能。因此，相干处理间隔（CPI）中的可重复数越高，感测任务的性能越好。相反，通信效率与可识别码的数量成反比。此外，周围的无线电环境通常是动态的，由于其高移动性而具有高不确定性，使得ICS的波形优化问题更具挑战性。为此，本文开发了一种新的ICS框架，该框架建立在马尔可夫决策过程和深度强化学习的最新先进技术基础上。通过这样做，在不需要预先完全了解周围环境的情况下，ICS-AV可以自适应地优化其波形结构（即，CPI中的帧数），以在周围环境的动态和不确定性下最大化感测和数据通信性能。大量的仿真结果表明，我们提出的方法可以提高联合通信和传感性能高达46.26%，与其他基线方法相比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
