Rice University engineers are developing a testing framework to assess the stability, interoperability, energy efficiency, and communication performance of software-based machine learning-enabled 5G radio access networks (RANs), according to a press release.
"As 5G networks evolve toward more software-centric architectures, there is a critical need for advanced testing methods to ensure robust real-time performance," said the announcement.
The research is funded by a $1.9 million grant from the National Telecommunications and Information Administration (NTIA), and is focusing on "communication and computing dimensions, considering the challenges posed by the inherent indeterministic behavior of such environments."
"Current testing methodologies for wireless products have predominantly focused on the communication dimension, evaluating aspects such as load testing and channel emulation," said Rahman Doost-Mohammady, assistant research professor of electrical and computer engineering and the project’s principal investigator. "But with the escalating trend toward software-based wireless products, it’s imperative that we take a more holistic approach to testing.
“Our answer to this critical challenge is ETHOS, an innovative testing framework that not only evaluates communication performance but also considers the impact of computing environments and the intricacies of machine learning on RAN software."
NTIA announced nearly $80 million in grants from the $1.5 billion Public Wireless Supply Chain Innovation Fund for the development of open and interoperable wireless networks.
“Open and interoperable wireless equipment will help drive competition, strengthen global supply chain resilience and lower costs for consumers and network operators, according to federal officials,” said the University.
Rice researchers will also test the framework’s efficacy and deploy machine learning algorithms for 5G RAN on the NVIDIA-supported Aerial Research Cloud (ARC) platform.
The team also “plans to engage existing industry contacts for feedback and fine-tuning of the framework, with a vision to expand collaborations as the project progresses.”
You can read more about this project and the researchers involved here.