标题： 面向基于太赫兹的障碍物感知：生成式无线环境感知框架 显示英文标题

标题： Towards THz-based Obstacle Sensing: A Generative Radio Environment Awareness Framework

摘要： 障碍物感知对于太赫兹（THz）通信至关重要，因为随后的波束管理可以避免THz信号被障碍物阻挡。 同时，无线电环境，可以通过信道知识（例如接收到的信号强度（RSS）分布）来体现，揭示了信号传播情况和相应的障碍物信息。 然而，在实践中，通过障碍物感知来了解无线电环境具有挑战性，因为稀疏部署的太赫兹传感器只能通过它们的RSS测量获得很少的先验知识。 因此，本文提出了一个无线电环境感知问题，这是首次考虑障碍物属性的概率分布。 为了解决此类问题，我们提出了一种基于太赫兹的生成无线电环境感知框架，在该框架中，可以直接从感知到的无线电环境中获取障碍物信息。 我们还提出了一种基于条件生成对抗网络（CGAN）的新颖生成模型，其中引入了U-net和问题的目标函数，以实现对RSS分布的准确感知。 仿真结果显示，所提出的框架可以提高无线电环境的感知能力，并因此在障碍物形状和位置方面实现优越的感知性能。 显示英文摘要

摘要： Obstacle sensing is essential for terahertz (THz) communication since the subsequent beam management can avoid THz signals blocked by the obstacles. In parallel, radio environment, which can be manifested by channel knowledge such as the distribution of received signal strength (RSS), reveals signal propagation situation and the corresponding obstacle information. However, the awareness of the radio environment for obstacle sensing is challenging in practice, as the sparsely deployed THz sensors can acquire only little a priori knowledge with their RSS measurements. Therefore, we formulate in this paper a radio environment awareness problem, which for the first time considers a probability distribution of obstacle attributes. To solve such a problem, we propose a THz-based generative radio environment awareness framework, in which obstacle information is obtained directly from the aware radio environment. We also propose a novel generative model based on conditional generative adversarial network (CGAN), where U-net and the objective function of the problem are introduced to enable accurate awareness of RSS distribution. Simulation results show that the proposed framework can improve the awareness of the radio environment, and thus achieve superior sensing performance in terms of average precision regarding obstacles' shape and location.