Mobile Devices

What 6G integrated with AI could enable

12 June 2024

A three-year project is aimed at investigating the role that artificial intelligence (AI)-assisted integrated sensing and communications will work together with next-generation cellular technology 6G.

This project from Southern Illinois University (SIU) is part of a larger initiative from the National Science Foundation that includes other 6G research from Villanova University and Aalto University of Finland.

While 5G technology has become the dominant cellular technology, 6G is beginning to ramp up and is expected to include sensing functions such as accurate location tracking and mapping, area imaging and operation of autonomous vehicles like “platooning” or operating several vehicles as one unit.

6G will also allow new functions such as detecting gestures for surveillance and identifying the people that make these gestures. These systems are aimed at extracting, classifying and predicting typical movements of the human body such as standing, sitting, walking, jumping, smiling or crying.

Researchers hope to create a design for integrating radio sensing and communications systems into a single hardware platform using a unified waveform and transmission strategy controlled by AI.

The integration of AI and 6G technologies could potentially:

  • Send a fleet of cars to transport several people
  • Help find missing people
  • Monitor environment and fight pollution
  • Expand remote sensing
  • Create smart cities

Challenges of integration

The challenges of integrating the two technologies are challenging, however.

“The electromagnetic spectrum or radio frequency band used for communication and sensing purposes is an extremely scarce and expensive resource,” said Gayan Aruma Baduge, associate professor in the School of Electrical, Computer and Biomedical. “Integrating communication and sensing tasks into the same hardware platform can greatly improve efficiency.”

This design could be cost effective as it could be reused for communication and sensing purposes, but these tasks have mutually competing design objectives. As a result, researchers propose to use a range of tools from communication and information theory, estimation and detection as well as data-driven AI to overcome the design challenges.

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