Machine Learning for Wireless Networking in Highly Dynamic Scenarios


Infraestructura FEDER - Universidad de Almería



The interest on using efficiently the Industrial, Scientific and Medical (ISM) spectrum bands for wireless communications is growing also among telecommunications operators offering broadband Internet access. However, due to the free-of-use conditions in ISM bands, and given their current and expected future congestion levels, offering service guarantees to the final users is a very challenging mission due to the complex and highly dynamic interactions between networks competing for the same spectrum resources.

In a network environment of such a complexity, heterogeneity and unpredictability, the current resource management strategies are not capable enough to keep the system working under optimal conditions. In addition, since networks will rarely be in steady state, the use of traditional techniques to analyze the system performance is unfeasible. Under these conditions, there is a growing interest in the use of machine learning techniques, capable of understanding the interactions between competing networks, exploring the effect of different actions, and intelligently choosing those that maximize the overall performance of the system.

Despite some recent interest by the research community and industry, the use of machine learning techniques for resource management in wireless networks is still in its infancy. One of the first questions to solve is where to implement those functionalities to maximize their potential. In the one hand, doing it at the cloud guarantees high computing power and large data availability, thus allowing to take accurate decisions about future network states, but at the cost of a large response delay. In the other hand, moving it to the edge limits both the available data and computational power, but allows to respond fast to a changing environment.

In that crossroad, this project aims to combine efficiently both approaches, i.e., short-term local decisions (based on partial information) with much more accurate but mid/long decisions (based on a global view of the network). This is still and unexplored research path with many challenges in front, including the design of compatible machine learning methods, the definition of the action domains between decision points, and a qualitative and quantitative performance evaluation that takes into account the actions costs and potential overheads, among others aspects.

Solving those challenges is the main goal of this project. We will design and evaluate a network architecture, new functionalities and protocols able to efficiently combine both decentralized and centralized optimization decisions based on the use of machine learning and data analytics. We will also characterize the interactions between local and global decision points (agents), studying both transient and stationary regimes (if exist), stability conditions, and their ability and speed to convergence to optimal solutions.

We expect the project will have a high impact in the research community, standardization committees, and industrial sector, providing both a theoretical and practical framework to test the proposed approach, new functionalities, and machine learning algorithms for key future dense WLAN scenarios. Using our approach we aim to significantly improve the spectrum utilization efficiency of ISM bands, with the goal to achieve a x10 gain with respect to the Quality of Experience observed by final users nowadays.



PhD thesis





Conferences / Workshops


Talks, Tutorials, Panels

  • IEEE CCNC 2023: “IEEE 802.11be and Beyond: All You Need to Know about Next-generation Wi-Fi”. With G. Geraci and L. Galati. Jan. 2023. 
  • IEEE Globecom 2022: "IEEE 802.11be and Beyond: All You Need to Know about Next-generation Wi-Fi". With G. Geraci and L. Galati. December 2022.
  • IEEE 802.11 plenary: “Wi-Fi Meets ML: Re-thinking Next-generation Wi-Fi Networks v2”. With S. Szott. Nov. 2022.
  • IEEE PIMRC 2022: “IEEE 802.11be and Beyond: All You Need to Know about Next-generation Wi-Fi”. With G. Geraci and L. Galati. Sept. 2022.
  • IEEE 802.11 AI/ML TIG: “Wi-Fi Meets ML: Re-thinking Next-generation Wi-Fi Networks”. With S. Szott. Sept. 2022. doc.:IEEE 802.11-22/1443r0
  • Moscow Telecommunication Seminar: WiFi 7: Scheduled Access, Multilink, and TXOP Sharing. Seminar Series organized by IITP RAS, Phystech, and Higher School of Economics. December 2021.
  • IEEE Globecom 2021: “A Primer on Wi-Fi 7: Objectives, Standardization, and Research”. With G. Geraci and L. Galati. December 2021.
  • IEEE ICC 2021 Industry Panel. ”IEEE 802.11be: Wi-Fi 7 Strikes Back”. Organized by G. Geraci and L. Galati, with A. Gowans (Ofcom), D. Sundman (Ericsson), and Bin Tian(Qualcomm). June 2021.
  • IEEE BlackSeeCom 2020. ”What Will Wi-Fi 7 be?”. With G. Geraci and L. Galati. May 2021.
  • IEEE Globecom 2020. ”Introducing IEEE 802.11be - the Wi-Fi of the future”. With G. Geraci and L. Galati. December 2020.



  • Simulator for dense Wi-Fi networks (Komondor) [link].
  • Flow-level simulator for Wi-Fi networks (Neko) [link].
  • Markovian models SFCTM [link]



  • Wi-Fi All Channels (WACA) [link]
  • Google Stadia traces [link]

Project funded by Spanish Gov. with FEDER funds.