Unleashing the potential of machine learning to address spatial reuse in future IEEE 802.11 WLANs: an introduction to two problem statements for the ITU AI Challenge

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Unleashing the potential of machine learning to address spatial reuse in future IEEE 802.11 WLANs: an introduction to two problem statements for the ITU AI Challenge

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  • Spatial Reuse (SR) is one of the techniques that is gaining more attention in next-generation IEEE 802.11 standards. SR was firstly introduced in the IEEE 802.11ax (11ax) as a decentralized mechanism, but it is now evolving with IEEE 802.11be (11be) thanks to the multi Access Point (multi-AP) feature. In both cases, SR aims at increasing the number of parallel transmissions in an Overlapping Basic Service Set (OBSS) by applying sensitivity adjustment and transmit power control. In this talk, we will provide an overview of both 11ax and 11be SR mechanisms and discuss two problem statements for the ITU AI Challenge, where participants must harness the potential of Machine Learning (ML) to solve a relevant problem in communications such as the SR one.

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      Speaker abstract

      AI/ML integration in Wi-Fi: an overview; The unlicensed spectrum is (and will continue to be) an essential part of wireless communications, as it provides ease of access and great flexibility to fulfill a plethora of use cases. Wi-Fi is one of the flagship technologies operating in the unlicensed bands and it is now facing fundamental changes toward unprecedented requirements such as ultra-high reliability. This talk will provide an overview of the current evolution path of Wi-Fi towards a deeper integration of AI/ML functionalities, highlighting activities discussing unified architecture, common standardized interfaces and strategical functionalities.
      Francesc Wilhelmi
      Postdoctoral Researcher, Mobile Networks Department
      Nokia Bell Labs
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