Researchers at 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, they promise a future of incredibly fast and reliable wireless connections. A key technology behind this 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 your device. This information is called “channel state information (CSI)”. The problem 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 your connection.
In this view, a team of researchers at Incheon National University led by Associate Professor Byungju have developed a new AI-powered solution. Their method, called “transformer-assisted parametric CSI feedback”, focuses 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. The paper was made available online on October 16, 2024, and published in Volume 23, Issue 12, December 2024 of the journal IEEE Transactions on Wireless Communications.
“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,” explains Prof. Byungju Lee.
The team leveraged artificial intelligence (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,” says Prof. Lee.
This innovative method sets a new benchmark for wireless communication, ensuring the reliability and speed required for next-generation networks.
Reference
Title of original paper: Transformer-Assisted Parametric CSI Feedback for mmWave Massive MIMO Systems
Journal: IEEE Transactions on Wireless Communications
