The AI-powered method improves high-speed users’ connectivity and reduces next-gen wireless system errors.
Researchers at South Korea’s Incheon National University have developed an innovative method to improve next-generation wireless networks.
Their approach ensures faster, more reliable connections by simplifying how large amounts of signal data are managed and using artificial intelligence to predict and correct errors. The findings promise significant benefits for high-speed travel, satellite communication and disaster response applications.
As 5G and 6G networks expand, a key technology is millimeter-wave (mmWave), which uses very high-frequency radio waves to transmit huge amounts of data. To make the most of mmWave, networks use large groups of antennas working together, called massive Multiple-Input Multiple-Output (MIMO).
However, managing these complex antenna systems is challenging. They require precise information about the wireless environments between the base station (like a cell tower) and devices. This information is called channel state information (CSI).
The issue is that these signal conditions change rapidly, especially when moving – in a car, train or even a drone. This rapid change, the ‘channel aging effect’ can cause errors and disrupt connections.
The Incheon research team has developed a new AI-powered solution – called transformer-assisted parametric CSI feedback – focused on key aspects of the signal instead of sending all the detailed information. It concentrates on a few key pieces of information including angles, delays and signal strength. By focusing on these key parameters, the system significantly reduces the amount of information that needs to be sent back to the base station.
“To address the rapidly growing data demand in next-generation wireless networks, it is essential to leverage the abundant frequency resource in the mmWave bands. In mmWave systems, fast user movement makes this channel ageing a real problem,” said teamleader Prof. Byungju Lee.
The team leveraged AI, specifically a transformer model, to analyze and predict signal patterns. Unlike older techniques like CNNs, transformers can track both short- and long-term patterns in signal changes, making real-time adjustments even when users are moving quickly.
A key aspect of their approach is prioritizing the most important information – angles and delays – when sending feedback to the base station. This is because these parameters have the biggest impact on the quality of the connection.
Tests showed that their method significantly reduced errors (over 3.5 dB lower error than conventional methods) and improved data reliability, as measured by bit error rate (BER).
The solution was also tested in diverse scenarios, from pedestrians walking at 3 km/h to vehicles moving at 60 km/h and even high-speed environments like highways. In all cases, the method outperformed traditional approaches.
This breakthrough can provide uninterrupted internet to passengers on high-speed trains, enable seamless communication in remote areas via satellites and enhance connectivity during disasters when traditional networks might fail.
It is also poised to benefit emerging technologies like vehicle-to-everything (V2X) communications and maritime networks.
“Our method ensures precise beamforming, which allows signals to connect seamlessly with devices, even when users are in motion,” said Prof. Lee.
