mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning

mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning

 
 
 IEEE Future Networks World Forum 2023 (Best Paper Award)
 mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning
 DOI: 10.1109/FNWF58287.2023.10520606

Using analog beamforming in mmWave frequency bands we can focus the energy towards a receiver to achieve high throughput. However, this requires the network to quickly find the best downlink beam configuration in the face of non-IID data. We propose a personalized Federated Learning (FL) method to address this challenge, where we learn a mapping between uplink Sub-6GHz channel estimates and the best downlink beam in heterogeneous scenarios with non-IID characteristics. We also devise FedLion, a FL implementation of the Lion optimization algorithm. Our approach reduces the signalling overhead and provides superior performance, up to 33.6 % higher accuracy than a single FL model and 6 % higher than a local model.

Suggested Citation

@inproceedings{isaksson2023mmwave,
    title         = {{mmWave Beam Selection in Analog Beamforming using Personalized Federated Learning}},
    author        = {Martin Isaksson and Filippo Vannella and David Sandberg and Rickard C\"{o}ster},
    year          = 2023,
    booktitle     = {{IEEE} Future Networks World Forum (FNWF)},
    note          = {(Best paper award)},
    publisher     = {{IEEE}},
    doi           = {10.1109/FNWF58287.2023.10520606}
}

Available

 mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning
 DOI: 10.1109/FNWF58287.2023.10520606
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