Researchers Hope to Revolutionize 5G Testing Using Machine Learning, $1.9M NTIA Grant

Researchers at Rice University hope to revolutionize 5G network testing by using machine learning and other technologies, as well as a $1.9 million grant from NTIA to develop a more comprehensive testing approach.

5G networks increasingly are software based. This makes it necessary to develop appropriate advanced testing methods to measure real-time performance in such networks. In creating such test regimes, Rice says that it is vital to focus on both communications and computing due to “the inherent indeterministic behavior of such environments.”

Enhancing 5G Network Testing

Rice is developing the ETHOS testing framework to assess the stability, interoperability, energy efficiency and communications performance.

“The broader impacts of this project are far-reaching, with the potential to revolutionize software-based and machine learning-enabled wireless product testing by making it more comprehensive and responsive to the complexities of real-world network environments,” Ashutosh Sabharwal, the Ernest Dell Butcher Professor of Engineering, chair of the Department of Electrical and Computer Engineering and the co-principal investigator of the project, said in a prepared statement.

“By providing the industry with advanced tools to evaluate and ensure the stability, energy efficiency and throughput of their products, our research is poised to contribute to the successful deployment of 5G and beyond wireless networks.”

After the testing framework is created, researchers from the university will deploy machine learning algorithms for 5G radio access networks on the Aerial Research Cloud (ARC) platform. The network research sandbox is supported by NVIDIA, a company that focuses on visual and accelerated computing.

The grant that Rice received came through NTIA’s Public Wireless Supply Chain Innovation Fund, which aims to boost U.S. wireless innovation. The nearly $80 million third round of grants was announced on January 10.


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