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.
翻译:最近,综合通信和遥感(ICS)已成为无处不在的遥感和IoT应用的赋能技术。对于ICS对自主车辆的应用,优化波形结构是因遥感和数据通信功能之间影响巨大而最具挑战性的任务之一。具体地说,数据通信框架的序言通常用于遥感功能。因此,协同处理跨度(CPI)中更多的序言是更大的遥感任务。相反,通信效率与序言数量成反比。此外,周围无线电环境的动态性能和高度的不确定性很大,使ICS的波形优化问题更加具有挑战性。为此,本文为Markov决策过程和最近先进的深层强化学习技术开发了ICS新颖的框架。这样,ICS-AV无需事先对周围环境的完整了解,就可以适应性地优化其波形结构(即CPI中的框架数目)。此外,周围无线电环境环境的高度移动性能通常具有动态性,使ICS的波形优化问题更加具有挑战性。为此,本文件为Markov决定过程和最近的先进技术制定了新的ICS框架。这样做,因此,ICS-AV可以调整其波状结构结构(即CPI),以便比较地模拟其他环境的动态和测测测测测测测测测测基方法。