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Cross Layer Optimization Using AI/ML-Assisted Federated Edge Learning in 6G Networks

Author

Listed:
  • Spyridon Louvros

    (Jio Platforms Limited (JPL), Miiduranna Village, 74015 Viimsi Parish, Estonia)

  • AnupKumar Pandey

    (Research & Development, Jio Platforms Limited, TC-22, 5th Floor, A Wing, Reliance Corporate Park, Thane Belapur Road, Ghansoli, Navi Mumbai 400701, Maharashtra, India)

  • Brijesh Shah

    (Research & Development, Jio Platforms Limited, TC-22, 5th Floor, A Wing, Reliance Corporate Park, Thane Belapur Road, Ghansoli, Navi Mumbai 400701, Maharashtra, India)

  • Yashesh Buch

    (Research & Development, Jio Platforms Limited, TC-22, 5th Floor, A Wing, Reliance Corporate Park, Thane Belapur Road, Ghansoli, Navi Mumbai 400701, Maharashtra, India)

Abstract

This paper introduces a novel methodology that integrates 6G wireless Federated Edge Learning (FEEL) frameworks with MAC to PHY cross layer optimization strategies. In the context of mobile edge computing, typically ensuring robust channel estimation within the 6G network use cases presents critical challenges, particularly in managing data retransmissions. Inaccurate updates from distributed 6G devices can undermine the reliability of federated learning, affecting its overall performance. To address this, rather than relying on direct evaluations of the objective function, we propose an AI/ML-assisted algorithm for global optimization based on radial basis functions (RBFs) decision-making process to assess learned preference options.

Suggested Citation

  • Spyridon Louvros & AnupKumar Pandey & Brijesh Shah & Yashesh Buch, 2026. "Cross Layer Optimization Using AI/ML-Assisted Federated Edge Learning in 6G Networks," Future Internet, MDPI, vol. 18(2), pages 1-42, January.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:2:p:71-:d:1853008
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