Eskandari, M;
Burr, AG;
Cumanan, K;
Wong, KK;
(2024)
CGAN-Based Slow Fluid Antenna Multiple Access.
IEEE Wireless Communications Letters
10.1109/LWC.2024.3452941.
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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.
| Type: | Article |
|---|---|
| Title: | CGAN-Based Slow Fluid Antenna Multiple Access |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LWC.2024.3452941 |
| Publisher version: | https://doi.org/10.1109/LWC.2024.3452941 |
| 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: | Antenna position selection, fluid antenna systems, machine learning, conditional generative adversarial networks, outage, fluid antenna multiple access |
| 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/10197478 |
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