{"id":652598,"date":"2025-02-07T12:30:29","date_gmt":"2025-02-07T09:30:29","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/ai-powered-method-improves-reliability-of-next-generation-networks\/"},"modified":"2025-02-07T12:30:29","modified_gmt":"2025-02-07T09:30:29","slug":"ai-powered-method-improves-reliability-of-next-generation-networks","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/ai-powered-method-improves-reliability-of-next-generation-networks\/","title":{"rendered":"#AI-powered method improves reliability of next-generation networks"},"content":{"rendered":"
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\n mmWave Massive MIMO system with moving UE; The angle-delay reciprocity holds in stationary (quasi-static) but not in non-stationary scenarios. Credit: IEEE Transactions on Wireless Communications<\/i> (2024). DOI: 10.1109\/TWC.2024.3476474
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As 5G and 6G networks expand, they promise a future of incredibly fast and reliable wireless connections. A key technology<\/a> 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).<\/p>\n

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\u2014in a car, train, or even a drone. This rapid change, the channel aging effect, can cause errors and disrupt your connection.<\/p>\n

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.<\/p>\n

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 published in the journal IEEE Transactions on Wireless Communications.<\/a><\/i><\/p>\n

“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 aging a real problem,” explains Prof. Byungju Lee.<\/p>\n

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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.<\/p>\n

A key aspect of their app<\/a>roach is prioritizing the most important information\u2014angles and delays\u2014when sending feedback to the base station. This is because these parameters have the biggest impact on the quality of the connection.<\/p>\n

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.<\/p>\n

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.<\/p>\n

“Our method ensures precise beamforming, which allows signals to connect seamlessly with devices, even when users are in motion,” says Prof. Lee.<\/p>\n

This innovative method sets a new benchmark for wireless communication, ensuring the reliability and speed required for next-generation networks.<\/p>\n

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More information:<\/strong>
\n\t\t\t\t\t\t\t\t\t\t\t\tHyungyu Ju et al, Transformer-Assisted Parametric CSI Feedback for mmWave Massive MIMO Systems, IEEE Transactions on Wireless Communications<\/i> (2024). DOI: 10.1109\/TWC.2024.3476474<\/a><\/p>\n<\/div>\n

\n Provided by
\n Incheon National University
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\n Citation<\/strong>:
\n AI-powered method improves reliability of next-generation networks (2025, February 6)
\n retrieved 7 February 2025
\n from https:\/\/techxplore.com\/news<\/a>\/2025-02-ai-powered-method-reliability-generation.html\n <\/p>\n

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