Researchers at the National Institute of Standards and Technology (NIST) say they 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.
According to NIST, 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, and 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 network data rate without communicating with each other. The scheme rapidly achieves overall performance that is close to the result based on exhaustive trial-and-error channel searches, according to NIST.
The study addressed indoor scenarios, such as a building with multiple Wi-Fi access points and cellphone operations in unlicensed bands.
NIST researchers said they now plan to model the method in larger-scale outdoor scenarios and conduct physical experiments to demonstrate the effect.