The field of wireless communication and sensing is rapidly evolving, with a focus on developing innovative solutions for accurate radio map construction, indoor localization, and environment-aware communication systems. Recent research has explored the use of machine learning and deep learning techniques to improve the accuracy of radio map construction and wireless channel prediction. Additionally, there has been a growing interest in developing flexible and scalable solutions for multi-robot systems and optical wireless communication networks.
Noteworthy papers in this area include RadioDiff-Inverse, which proposes a diffusion-enhanced Bayesian inverse estimation framework for radio map construction, and NeRF-APT, which presents a novel NeRF framework for wireless channel prediction. FERMI is also noteworthy as it introduces a flexible radio mapping framework that combines physics-based modeling with a neural network to capture environmental interactions with radio signals.
