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

TL;DR

This paper introduces a novel generative framework using CGANs to improve obstacle sensing in THz communication by better understanding the radio environment from limited RSS data.

Contribution

It proposes the first probabilistic radio environment awareness model for obstacle sensing and develops a CGAN-based method for accurate RSS distribution estimation.

Findings

Enhanced radio environment awareness improves obstacle detection accuracy.

The framework achieves higher average precision in obstacle shape and location.

Simulation results validate the effectiveness of the proposed approach.

Abstract

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.