CGAN-Based Slow Fluid Antenna Multiple Access

Mahdi Eskandari*, Alister Graham Burr, Kanapathippillai Cumanan, Kai Kit Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
JournalIEEE wireless communications letters
Early online date2 Sept 2024
DOIs
Publication statusE-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

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