Advancing THz Radio Map Construction and Obstacle Sensing: An Integrated Generative Framework in ISAC

Integrated sensing and communication (ISAC) in the terahertz (THz) band enables obstacle detection, which in turn facilitates efficient beam management to mitigate THz signal blockage. Simultaneously, a THz radio map, which captures signal propagation characteristics through the distribution of received signal strength (RSS), is well-suited for sensing, as it inherently contains obstacle-related information and reflects the unique properties of the THz channel. This means that communication-assisted sensing in ISAC can be effectively achieved using a THz radio map. However, constructing a radio map presents significant challenges due to the sparse deployment of THz sensors and their limited ability to accurately measure the RSS distribution, which directly affects obstacle sensing. In this paper, we formulate an integrated problem for the first time, leveraging the mutual enhancement between sensed obstacles and the constructed THz radio maps. To address this challenge while improving generalization, we propose an integration framework based on a conditional generative adversarial network (CGAN), which uncovers the manifold structure of THz radio maps embedded with obstacle information. Furthermore, recognizing the shared environmental semantics across THz radio maps from different beam directions, we introduce a novel voting-based sensing scheme, where obstacles are detected by aggregating votes from THz radio maps generated by the CGAN. Simulation results demonstrate that the proposed framework outperforms non-integrated baselines in both radio map construction and obstacle sensing, achieving up to 44.3% and 90.6% reductions in mean squared error (MSE), respectively, in a real-world scenario. These results validate the effectiveness of the proposed voting-based scheme.

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

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.