ML4WIFI - ML-driven Radio Resource Management in Wireless Local Area Networks


  • TU Berlin
  • AGH University of Science and Technology

Team @ TKN


  • DFG (Deutsche Forschungsgemeinschaft)

Project Time

  • 12/2021 - 05/2026


Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access due to freedom of deployment and configuration (thanks to operating in unlicensed bands) and affordable and highly interoperable devices. However, the unplanned deployment and distributed management of WLANs operating in shared unlicensed spectrum is becoming an emerging challenge with the densification of these networks. Another challenge is related to technical innovations, which are making the next-generation of this technology exceedingly complex. Specifically, each new mechanism, designed to improve network performance, comes with a plethora of parameters which have to be properly configured to achieve the best results (and this configuration is left out of the standard). In most cases, multiple parameters have to be tuned together, which is a non-trivial task as the dependencies between parameters and their joint optimization have a highly non-linear impact on network performance. The level of complexity is further increased in the case of coexisting networks, where diverse parameters have to be set across multiple nodes that serve various applications with different QoS requirements. Indeed, future WLAN generations are anticipated to accommodate not only high throughput but also low latency and high-reliability traffic. In summary, the problem of next-generation WLANs is that traditional radio resource management (RRM) algorithms fail to guarantee a reasonable level of performance across a range of scenarios characterized by unplanned deployments and distributed management under the increasing number of configuration options.

All these factors make ML algorithms a perfect fit for modern networking, i.e., it can provide estimated models with tunable accuracy, help in tackling existing problems, and encourage new solutions potentially leading to breakthroughs. However, the application of ML algorithms to solve the problems of modern wireless networks poses certain challenges. For example, it requires the definition of the environment state (i.e., observation space), the action space, as well as the reward function for the RRM problem, which is not an obvious task but has a critical impact on the learning process and network performance. This and other ML-related challenges are being slowly overcome in 5G networks operating in licensed bands. Nevertheless, solutions designed for 5G will not be directly applicable to WLANs due to their characteristic differences. First, 5G uses a centralized management approach with carefully planned deployment. Meanwhile, WLANs use a distributed management approach where the deployment is in most cases unplanned and chaotic. Second, 5G operates in licensed bands, with no outside interference, whereas WLANs operate in shared bands where they interfere with each other as well as with devices of other technologies. The ML4WIFI project will address those issues.

Selected Publications

  1. Anatolij Zubow, Muhammad Elhwawshy, Sascha Rösler, Lorenz Pusch, Adam Wolisz and Falko Dressler, "SensingWall: Ultra-low Cost WiFi Wireless Sensing," Proceedings of 43rd IEEE Conference on Computer Communications (INFOCOM 2024), Demo Session, Vancouver, Canada, May 2024. [BibTeX, PDF, More details]
  2. Piotr Gawłowicz, Anatolij Zubow and Falko Dressler, "Matryoshka: Single RF Chain Multi-user Transmission through WiFi-in-WiFi Signal Emulation using COTS Hardware," Proceedings of 29th ACM International Conference on Mobile Computing and Networking (MobiCom 2023), 17th ACM International Workshop on Wireless Network Testbeds, Experimental evaluation and Characterization (WINTECH 2023), Madrid, Spain, October 2023, pp. 96–103. [DOI, BibTeX, PDF, More details]
  3. Wojciech Ciezobka, Maksymilian Wojnar, Katarzyna Kosek-Szott, Szymon Szott and Krzysztof Rusek, "FTMRate: Collision-Immune Distance-based Data Rate Selection for IEEE 802.11 Networks," Proceedings of 24th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2023), Boston, MA, June 2023. [DOI, BibTeX, More details]
  4. Katarzyna Kosek-Szott, Szymon Szott and Falko Dressler, "Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches," IEEE Access, vol. 10, pp. 120285–120295, November 2022. [DOI, BibTeX, PDF, More details]
  5. Szymon Szott, Katarzyna Kosek-Szott, Piotr Gawłowicz, Jorge Torres Gómez, Boris Bellalta, Anatolij Zubow and Falko Dressler, "Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning," IEEE Communications Surveys & Tutorials, vol. 24 (3), pp. 1843–1893, July 2022. VDE ITG Preis 2023 [DOI, BibTeX, PDF, More details]
  6. Szymon Szott, Katarzyna Kosek-Szott, Piotr Gawłowicz, Jorge Torres Gómez, Boris Bellalta, Anatolij Zubow and Falko Dressler, "WiFi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning," arXiv, cs.NI, 2109.04786, September 2021. [DOI, BibTeX, PDF, More details]
Last modified: 2024-06-11