Intelligent Radio Resource and Mobility Management based on Federated Learning

The project aims to develop a novel artificial intelligence-based radio resource and mobility management concept exploiting federated learning with the individual management processes handled via cooperating machine learning-based algorithms.

6G, Radio resource management, Mobility management, Machine learning, Federated learning, Artificial intelligence

Major results and publications

I. Ahmad, Z. Becvar and P. Mach
Coordinated Machine Learning for Channel Reuse and Transmission Power Allocation for D2D Communication
IEEE Global Communications Conference (IEEE Globecom 2024), 2024.
I. Ahmad, Z. Becvar, P. Mach and D. Gesbert
Coordinated Machine Learning for Energy Efficient D2D Communication
IEEE Wireless Communications Letters, volume 13, no. 5, 2024.
P. Skaba, Z. Becvar, P. Mach and I. Guvenc
Coordinated Machine Learning for Handover in Mobile Networks with Transparent Relaying UAVs
IEEE International Conference on Communications (IEEE ICC 2024), workshop AI/ML-Powered Autonomous Telco Networks (APATN), 2024.
P. Mach, Z. Becvar and M. Nikooroo
Multi-hop Relaying with Mixed Half and Full Duplex Relays for Offloading to MEC
IEEE Global Communications Conference (IEEE Globecom 2023), Workshop on Emerging Topics in 6G Communications (6GComm), 2023.

Date:: 01/2023 - 12/2025
Project No.:: 23-05646S
Funding:: Czech Science Foundation (GACR)
Budget:: ~250 kEUR
PI:: Zdenek Becvar
Team (6Gm):: Pavel Mach, Ishtiaq Ahmad, Ramsha Narmeen, Zubair Khaliq
Partners:: ...
Topics:: 6G, Radio resource management, Mobility management, machine learning, federated learning

-Ongoing-6G ML/AI Mobility RRM TestbedIntelligent Radio Resource and Mobility Management based on Federated Learning

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