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Advancing AI Learning for Wireless Communications Testing 


When development work began on 6G, artificial intelligence (AI) was identified as one of the critical technologies needed to enable the next generation of wireless communications. This was because AI had the potential to address some of the more complex technical challenges facing the industry. But like many new technologies, making AI work for mobile communications requires close collaboration across government, industry, business, and academia.

To assist firms interested in deploying AI to enhance their businesses, the Commonwealth of Massachusetts has launched an innovative pilot program called AI Jumpstart. This novel program funds new computing infrastructure at Boston’s Northeastern University (NU) to connect industry partners with world-class facilities and university researchers across several AI disciplines. As an industry partner, Keysight has been involved in the project since funding was announced.

As a partnership between the state economic development agency Massachusetts Technology Collaborative and Northeastern, the AI Jumpstart is based on a 15-year relationship that has invested in university-based research and development projects that have successfully engaged business and industry. Through this program, Keysight has created a valuable public-private partnership to collaborate on AI research with Northeastern.

“The AI Jumpstart program is a powerful model of collaboration between industry, academia and the Mass Tech/John Adams Innovation Institute,” said Michael B. Silevitch, Northeastern University’s Principal Investigator of AI Jumpstart. “Our collaboration with Keysight provided a platform that demonstrated the immense viability of the approach.”

Advancing AI Learning

With an eye towards enabling 5G+ and 6G, Keysight and Northeastern worked together to identify a technical challenge that AI could help solve: How do wireless communications systems need to adjust to co-exist with radar signals?

“Keysight worked with Northeastern researchers and students to define the scope, scenarios, and data sets that will help to optimize test techniques for wireless network deployments,” said Josep M. Jornet, Associate Director of NU Institute for the Wireless Internet of Things (WIoT). “The project invoked AI learning from a 5G data set generated in Northeastern University’s RF Colosseum and a representative radar defined through Keysight’s PathWave Software.”

Once the scenarios were designed, the high-priority radar signal created by Keysight was introduced into the RF Colosseum’s real-time LTE commercial spectrum scenario of Rome, Italy.

The output of this was an I / Q data set that was then used for AI learning.  The AI algorithm processed these I / Q samples analyzing for throughput, power, and resource allocations within the 5G network. A sampling of the data samples is shown below:

High-priority radar signals created through Keysight PathWave were introduced into NU RF Colosseums spectrum simulation for AI learning.   

Tommaso Melodia, Director of NU Institute for the WIoT explains why this research is important: “Keysight and NU share common radio frequency/microwave teaching and testing goals for wireless research and industrial applications. Spectrum sharing is going to be a key technology for 5G and beyond, and AI learning was performed on I/Q data samples to enable spectrum sharing through optimal resource allocation to maximise throughput and minimize power consumption.”

Bringing AI Learning to Industry 

Through the AI-Jumpstart analysis, Keysight has gained a better understanding of the testing needs of wireless and defense developers as spectrum operations advance toward wider bandwidths, higher frequencies, and more complex environments. This understanding will drive investment in updates to Keysight’s test solution hardware and software designs as well as incorporating AI / ML into future test applications.

“The AI-learned patterns will assist Keysight in developing improved wideband, real-time capture analysis and closed-loop test applications,” said Roger Nichols, Keysight Technologies’ 6G Program Manager. “Keysight will be able to incorporate these into new spectral management testing solutions to help designers more quickly and accurately identify, classify, and prioritize signals and waveform characteristics of interest.”

In addition, Nichols said that early adoption of these AI test advances will allow commercial and defense developers to validate new hardware, software/firmware, and signal processing algorithms early so their performance can be demonstrated and proven in complex and evolving spectral sharing and coexistent environments.

Enabling Student Learning

While the primary focus of the AI Jumpstart project is on assisting businesses in deploying AI, the project has also been valuable in defining the training needed to student and industry engineers for this new technology.

“We launched this program on behalf of Massachusetts to boost the adoption and integration of AI by companies in our state, by bringing them together with the leading researchers at Northeastern,” said Pat Larkin, Director, Innovation Institute at MassTech. “We’re excited by the progress on Keysight’s project, as it shows the direct impact this research on the company’s products, but equally important, the strong relationship that was built with a talent-development center like Northeastern, where students get real-world expertise working on projects like this.”

“It is incredibly valuable to have industry partners such as Keysight involved in mmWave and 6G wireless technology testing, teaching and research are advancing to optimize and define wireless network standards and performance,” said Carey Rappaport, Northeastern University AI Jumpstart Director. “Keysight’s core RF & microwave measurement science knowledge, combined with NU’s THz experts and testbeds, inspire students toward relevant classes and careers and supports MA industry engineers with teaching and test techniques.”

For Nichols, this is simply how Keysight likes to partner to create new technologies.

“We are excited to help the radio frequency / microwave workforce grow through deeper understanding of the impacts of software, firmware, and algorithm designs on communication link performance,” he said. “Through this ongoing, collaborative research project, we are discovering how to use and deploy these technologies while giving the next generation of engineers the technical skills needed to make the next leap.”