He, C;
Lu, Y;
Chen, W;
Ai, B;
Wong, KK;
Niyato, D;
(2025)
Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach.
IEEE Transactions on Vehicular Technology
pp. 1-6.
10.1109/TVT.2025.3570319.
(In press).
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Abstract
An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an “unsupervised learning to optimize” paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into an unsupervised loss function to train the two-stage GNN. The numerical results demonstrate that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately.
| Type: | Article |
|---|---|
| Title: | Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TVT.2025.3570319 |
| Publisher version: | https://doi.org/10.1109/tvt.2025.3570319 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | FAS, unsupervised learning, MU-MISO, twostage GNN. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209042 |
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