Credit: NIST
NIST engineer Jason Coder makes mathematical calculations for a machine learning formula that may help 5G and other wireless networks select and share communications frequencies efficiently.
Researchers at the National Institute of Standards and Technology (NIST) have developed a mathematical formula that, computer simulations suggest, could help 5G and other wireless networks select and share communications frequencies about 5,000 times more efficiently than trial-and-error methods.
The novel formula is a form of machine learning that selects a wireless frequency range, known as a channel, based on prior experience in a specific network environment. Described at a virtual online conference this week, the formula could be programmed into software on transmitters in many types of real-world networks.
The NIST formula is a way to help meet growing demand for wireless systems, including 5G, through the sharing of frequency ranges, also known as bands, that are unlicensed. Wi-Fi, for example, uses unlicensed bands — those not assigned by the Federal Communications Commission to specific users. The NIST study focuses on a scenario in which Wi-Fi competes with cellular systems for specific frequencies, or subchannels. What makes this scenario challenging is that these cellular systems are raising their data-transmission rates by using a method called License Assisted Access (LAA), which combines both unlicensed and licensed bands.
“This work explores the use of machine learning in making decisions about which frequency channel to transmit on,” NIST engineer Jason Coder said. “This could potentially make communications in the unlicensed bands much more efficient.”
The NIST formula enables transmitters to rapidly select the best subchannels for successful and simultaneous operation of Wi-Fi and LAA networks in unlicensed bands. The transmitters each learn to maximize the total n ..
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