- High frequency signals collapse when walls or people block their path
- Neural networks learned the beam flexion simulating innumerable basketball practice shots
- Metasurfaces integrated into signals in transmitters with extreme precision
For years, researchers have fought with some vulnerabilities in ultra frequency communications.
Ultrahight frequencies are so fragile that signals that promise a huge bandwidth can collapse when they face still modest obstacles, since the walls, shelves or simply move people can stop avant -garde transmissions.
However, a new approach to Princeton engineers suggests that these barriers may not be permanent obstacles, although the jump from the experiment to the deployment of the real world remains uncertain.
From physics experiments to adaptive transmissions
The idea of folding signals to avoid obstacles is not new. Engineers have worked for a long time with “aerated beams”, which can be curved in a controlled way, but apply them to wireless data has been hindered by practical limits.
Haoze Cen, one of the researchers, says that most of the previous work focused on showing the beams that could exist, not to make them usable in unpredictable environments.
The problem is that each curve depends on innumerable variables, without leaving a direct way to scan or calculate the ideal route.
To make the beams useful, the researchers borrowed a sports analogy. Instead of calculating each shot, basketball players learn through repeated practice what works in different contexts.
Chen explained that the Princeton team pointed to a similar process, replacing the trial and error athletes with a neuronal network designed to adapt their answers.
Instead of physically transmitting beams for each possible obstacle, doctoral student Atsutse Kludze built a simulator that allowed the system to virtually.
This approach greatly reduced the training time while still bases the models on the physics of the aired beams.
Once trained, the system was able to adapt extremely fast, using a specially designed goals to shape the transmissions.
Unlike reflectors, which depend on external structures, the surface goals can be integrated directly into the transmitter, which allowed the beams to curve around sudden obstructions, maintaining connectivity without requiring a clear line of vision.
The team showed that the neuronal network could select the most effective beam route in disorderly and changing scenarios, something that conventional methods cannot achieve.
He also states that this is a step to take advantage of the Sub -itertz band, a part of the spectrum that could admit up to ten times more data than current systems.
The principal researcher Yasaman Ghasempour argued that addressing obstacles is essential before such bandwidth can be used for demanding applications such as immersive virtual reality or totally autonomous transport.
“This work addresses a long -standing problem that has prevented the adoption of so high frequencies in dynamic wireless communications to date,” said Ghasempour.
Even so, the challenges remain. The translation of laboratory demonstrations on commercial devices requires scaling hardware, refining training methods and demonstrating that adaptive beams can handle the complexity of the real world at speed.
The promise of wireless links that approach the performance of the Terabit class can be visible, but the path around obstacles, both physical and technological, is still winding.
Through Techxplore
