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ETHOS unleashed: Revolutionizing 5G network testing
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5G networks are progressing toward more software-centric architectures. There’s a shift from traditional hardware-centric network structures to those where software plays a more prominent role in managing and controlling network functions.
Increasing expectations from mobile users regarding the quality of their 5G experience are leading to higher demands on network capacity and performance. There is a critical need for advanced testing methods to ensure robust real-time performance.
In a bid to reshape the landscape of global wireless networks, researchers at Rice University in Texas are crafting a testing framework called ETHOS, which will test the stability, interoperability, energy efficiency, and communication prowess of software-based machine learning-infused 5G radio access networks (RANs).
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Revolutionizing 5G network testing
The 5G RAN provides wireless connectivity between user devices like smartphones, tablets, and IoT devices and the core network, enabling users to access various services and connect.
ETHOS makes for a clever way of testing, which doesn't just look at how well devices communicate with each other but also pays attention to how the computer systems and the special features of machine learning affect the software that manages wireless networks.
“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,” added Doost-Mohammady.
The standout features of 5G connectivity - fast speeds, low latency, and greater bandwidth - play a key role in providing an optimal and satisfying user experience.
Next, the team plans to test it to see how well it works. They'll use machine learning algorithms for 5G RAN on the NVIDIA-supported Aerial Research Cloud (ARC) platform.
The ARC platform is made specifically for the latest wireless technology. It makes it easier for developers to start and create new programs that work in real-time networks.
“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,” said Ashutosh Sabharwal, co-principal investigator of the project.
“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,” added Sabharwal.
The project received financial support through a $1.9 million grant from the U.S. Department of Commerce’s National Telecommunications and Information Administration (NTIA).
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