Core Viewpoint - Princeton University's research team has developed an innovative "curved wave" system capable of transmitting ultra-high frequency signals rapidly and stably, addressing the challenges posed by the increasing demand for data and the Internet of Things [1][2]. Group 1: Technology and Innovation - The new system utilizes a neural network to dynamically shape the transmission path of wireless signals, allowing them to bypass obstacles and maintain stable, high-speed communication [1]. - The technology focuses on the sub-terahertz frequency band, which has the potential to transmit ten times the data volume of current wireless systems, making it crucial for high-bandwidth applications such as virtual reality and fully autonomous vehicles [1]. - Traditional methods rely on external reflectors to navigate around obstacles, which are often unreliable or difficult to deploy in real-world scenarios [2]. Group 2: Methodology - The research team employs a special radio wave technology known as "Airy beams," which can propagate along curved trajectories rather than in straight lines, enabling effective transmission in non-line-of-sight conditions [2]. - A neural network is introduced to select and optimize the best curved path in real-time, similar to how professional basketball players make shooting decisions based on experience rather than complex calculations [2]. - A high-fidelity simulator was developed to allow the neural network to learn efficiently in a virtual environment, adapting to various obstacle layouts and dynamic changes [2]. Group 3: Future Implications - This work addresses a long-standing challenge in high-frequency wireless communication within dynamic environments, paving the way for future transmitters to intelligently navigate complex settings [2]. - The advancements in this technology are expected to support ultra-fast and highly reliable wireless connections for applications that are currently difficult to realize, such as immersive virtual reality and fully autonomous transportation [2].