Partnership enables the use of artificial intelligence and machine learning in advanced wireless communications systems.
Keysight and the University of Malaga (UMA) are collaborating to enable the use of artificial
intelligence (AI) and machine learning (ML) in advanced wireless communications systems. The partnership, which is focused on 6G research and development, has developed a method to import these algorithms into design and measurement tools to validate their performance and accelerate industry adoption.
“Many network operators and vendors are already using AI and machine learning in their
networks. But, so far, there has been no support from wireless standards on how AI and
machine learning should be deployed,” said Javier Campos, R&D Engineer at Keysight. “This
situation creates a lot of interoperability problems among equipment vendors and having a standard that isn’t optimized for AI / ML technology – this is the challenge we have been
working with the university to solve.”
Optimizing performance with AI
One of the major ways to optimize wireless performance is by providing and using accurate
channel state information (CSI) – or the known channel properties and conditions of the
communications link. CSI is used in real-time throughout a wireless system to adapt
transmissions as best as possible to current channel conditions with the goal of maintaining the best performance. This is a crucial component to achieving reliable communications with high data rates and multi-antenna systems. Traditionally, calculating and reporting accurate CSI is computationally and resource intensive making it a good candidate to begin integrating AI and ML into the network.
“While AI has been around for quite a long time, the industry has now been able to identify
concrete use cases, like optimizing CSI feedback, where AI can deliver huge gains in
performance, resource utilization, and energy efficiency,” said Campos.
Identifying CSI as a use case that would benefit from AI and ML, wireless researchers at UMA developed an AI / ML model for CSI feedback enhancement.
“What we are doing is reducing the information you have to send over the air to provide
accurate CSI from the user equipment to the base station,” said Mari Carmen Aguayo,
Professor, Universidad de Málaga and Head of the Institute of Telecommunication Research
(TELMA). “We are doing this by using AI algorithms that can squeeze the information into very small quantities, sending the minimum information required to maintain the best performance.”
When it came time to validate that their ML model performed better than traditional digital signal processing (DSP) for CSI feedback, they turned to Keysight for a digital twin platform to emulate the performance of their model in real world conditions. Building an interface layer that connected to Keysight’s PathWave System Design (SystemVue) modeling tool, UMA researchers were able to evaluate the model under a wide array of fading profiles and other test conditions to prove this method outperforms traditional approaches.
Jointly working for AI / ML-based standards
Thanks to this new interface, any AI / ML algorithm that adheres to the most common AI / ML APIs and frameworks can now be imported into Keysight’s SystemVue and used by the entire industry. To assist in this process, Keysight and UMA are working together to bring this innovation to the 3GPP RAN-1 standards body.
“Together we are looking forward to progressing in the different areas that 3GPP are studying,” said Campos. “We are working to have better usability and better measurements, so we can get the insights needed to bring this new technology to the industry.”
Following the validation of the AI models, UMA and Keysight presented them and the
associated measurement best practices to the 3GPP as part of Release 18, the first 3GPP
release studying AI/ML enhancements for the air interface. This ongoing partnership is
continuing its work with plans to make more contributions in the future and extend these findings to even more Keysight tools to enable wireless researchers everywhere.
“What makes this collaboration special is that we were able to connect our researchers with
measurement experts in the industry,” said Aguayo. “This kind of intersection yields exciting innovation for wireless communications.”
