Programming the issue of time
To date, existing software that directs resources through the radio access network (RAN)—mobile users’ gateway to the internet, beginning with radio base stations and leading downstream to user devices—struggles to keep up with the fast-paced wireless environment and the needs of clients. Changes in clients’ needs happen within milliseconds, but today’s RAN intelligent controllers (RIC)—AI and machine learning platforms that monitor the state of wireless demand and redirect resources accordingly—can take up to 10 milliseconds to respond.
That, and the heavy lift from having to handle complex calculations, can lead to disruptions in cellular network functions and create bottlenecks in internet coverage.
Building on previous work funded by the National Science Foundation, Bharadia, Shakkottai, Ghosh, Ko and colleagues designed EdgeRIC to have a two-way connection to the RAN. Using built-in custom microapps, or μApps, EdgeRIC monitors and tracks changes in the RAN in real-time, and can choose how to respond just as quickly. EdgeRIC then sends decisions in real-time to the RAN, prioritizing users based on the specific needs of their applications.
Reducing the amount of time it takes for software to read the wireless environment and then respond within 1 millisecond was the team’s greatest challenge, Ghosh said, but the problem had a surprisingly straightforward solution. The team decoupled EdgeRIC from the RAN, running it separately but beside a base station’s processing and storage unit, called a compute node, where it could interact with the RAN through a standard compatible interface. This decoupling had the advantage of allowing EdgeRIC to handle complex computations without overloading and disrupting the RAN.
Building on previous work funded by the National Science Foundation, Bharadia, Shakkottai, Ghosh, Ko and colleagues designed EdgeRIC to have a two-way connection to the RAN. Using built-in custom microapps, or μApps, EdgeRIC monitors and tracks changes in the RAN in real-time, and can choose how to respond just as quickly. EdgeRIC then sends decisions in real-time to the RAN, prioritizing users based on the specific needs of their applications.
Reducing the amount of time it takes for software to read the wireless environment and then respond within 1 millisecond was the team’s greatest challenge, Ghosh said, but the problem had a surprisingly straightforward solution. The team decoupled EdgeRIC from the RAN, running it separately but beside a base station’s processing and storage unit, called a compute node, where it could interact with the RAN through a standard compatible interface. This decoupling had the advantage of allowing EdgeRIC to handle complex computations without overloading and disrupting the RAN.
In tests, the team found that EdgeRIC’s μApps outpaced the amount of data received by existing, cloud-based, near real-time RICs by 5 to 25%. They observed a 30% increase in metrics used to measure an internet user’s experience, such as the smoothness of video streaming.
“EdgeRIC was meticulously developed and tested using real-world data collected at the TAMU Innovation Proving Ground,” said Ko. “By leveraging diverse scenarios involving drones, cars, autonomous robots, and human-scale mobility, we have been able to create robust and optimized AI algorithms.”
EdgeRIC also includes a built-in function that allows researchers to train the software offline, allowing for improvement toward the ultimate goal of preventing lag, dropped video calls and other detractions to users’ online experience.
“At the end of the day, our ultimate goal is to satisfy the end user and better understand their needs,” said Ghosh.
Ushering in a new generation of cellular networks
At NSDI 2024 in Santa Clara, California, Ghosh says EdgeRIC received a positive response, as others indicated an interest in real-time AI as the basis for a new way of interacting with RANs.
For now, Ghosh says, the team will continue to fine-tune EdgeRIC’s AI algorithms and research policies in AI for a stronger product. Next week, she and Ko will lead attendees of the Open AI Cellular and EdgeRIC workshop in hands-on training sessions to integrate and use their platform with existing software and hardware. Ideally, Ghosh would like EdgeRIC to represent a “one stop shop” for intelligent solutions to problems arising in RANs, for a smoother experience in 5G.
