- High -frequency signals collapse when walls or people block their way
- Neural networks learned beam bending by simulating countless basketball exercises
- Metasurfaces integrated into transmitters formed signals with extreme precision
For years, scientists have been struggling with some vulnerabilities in ultra -high -frequency communication.
Ultrahigh frequencies are so fragile that signals that promise huge bandwidth can collapse when confronted with even modest obstacles, as walls, shelves or simply moving people can bring groundbreaking transmissions to stop.
However, a new approach from Princeton engineers suggests that these barriers may not be permanent roadblocks, although the jump from experiment to the real world is still uncertain.
From physics experiments to adaptive transmissions
The idea of bending signals to avoid obstacles is not new. Engineers have long worked with “airy beams” which can curl in controlled ways, but applying them to wireless data has been hampered by practical boundaries.
Haoze Chen, one of the researchers, says that most previous work focusing on showing that the beams could exist, not making them useful in unpredictable environments.
The problem is that each curve depends on countless variables that do not leave any straightforward way of scanning or calculating the ideal path.
To make the beams useful researchers borrowed an analogy from sports. Instead of calculating each shot, basketball players learn through repeated practice what works in different contexts.
Chen explained the Princeton team aimed at a similar process that replaced athletes with trial-and-failure with a neural network designed to customize its answers.
Instead of physical transmission of beams for any possible obstacle, Doctor -Randatsuts Kludze built a simulator that allowed the system to practice practically.
This approach reduced the training time greatly, while still grounding the models in the physics of airy beams.
Once the system was trained, the system was able to adapt extremely quickly using a specially designed meta -surface to shape transmissions.
Contrary to reflectors that depend on external structures, meta -surface can be integrated directly into the transmitter, which enabled beams to curl around sudden obstacles, which maintains connection without requiring clear visual line.
The team demonstrated that the neural network could choose the most effective beam path in messy and changing scenarios, some conventional methods cannot achieve.
It also claims that this is a step towards utilizing the Under-Terahertz band, part of the spectrum that can support up to ten times more data than today’s systems.
Leading investigator Yasaman Ghasempour claimed it is important to tackle obstacles to obstacles obstacles before such a bandwidth can be used to require applications such as immersive virtual reality or fully autonomous transport.
“This work tackles a long -term problem that has prevented the adoption of such high frequencies in dynamic wireless communication to date,” Ghasempour said.
Still, there are still challenges. Translation of laboratory demonstrations into commercial devices requires scaling of hardware, refinement of training methods, and proving that adaptive beams can handle complexity in the real world with speed.
The promise of wireless connections approaching the terabit class flow can be visible, but the path of the obstacles, both physical and technological, is still winding.
Via Techxplore
