Abstract
The emerging fluid antenna system (FAS) technology enables multiple access utilizing deep fades in the spatial domain. This paradigm is known as fluid antenna multiple access (FAMA). Despite conceptual simplicity, the challenge of finding the position (a.k.a. port) that maximizes the signal-to-interference plus noise ratio (SINR) at the FAS receiver output, cannot be overstated. This letter proposes to take only a few SINR observations in the FAS space and infer the SINRs for the missing ports by employing a conditional generative adversarial network (cGAN). With this approach, port selection for FAMA can be performed based on a few SINR observations. Our simulation results illustrate great reductions in the outage probability (OP) with only few observed ports, showcasing the efficacy of our proposed scheme.
Original language | English |
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Journal | IEEE wireless communications letters |
Early online date | 2 Sept 2024 |
DOIs | |
Publication status | E-pub ahead of print - 2 Sept 2024 |
Bibliographical note
This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.Keywords
- Antenna position selection
- conditional generative adversarial networks
- fluid antenna multiple access
- fluid antenna systems
- machine learning
- outage