INTELLIGENT BRANDS // Mobile Technology

The AI-powered method improves highspeed users ’ connectivity and reduces nextgen wireless system errors .

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 team leader 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-toeverything ( 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 p .

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