A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs

  • Wee Kiat New
  • , Kai Kit Wong
  • , Hao Xu
  • , Chao Wang
  • , Farshad Rostami Ghadi
  • , Jichen Zhang
  • , Junhui Rao
  • , Ross Murch
  • , Pablo Ramirez-Espinosa
  • , David Morales-Jimenez
  • , Chan Byoung Chae
  • , Kin Fai Tong

Research output: Contribution to journalArticlepeer-review

143 Citations (Scopus)

Abstract

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimation methods, information-theoretic insights, new multiple access techniques, and hardware designs. Moreover, we delineate the challenges of FAS and explore the potential of using FAS to improve the performance of other contemporary technologies. By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS.

Original languageEnglish
Pages (from-to)2325-2377
Number of pages53
JournalIEEE Communications Surveys and Tutorials
Volume27
Issue number4
DOIs
Publication statusPublished - 2025
Externally publishedYes

Keywords

  • 6G
  • antenna
  • artificial intelligence
  • circuit
  • communications
  • deep learning
  • diversity gain
  • extreme connectivity
  • fluid antenna system
  • machine learning
  • multiple-input multiple-output
  • multiplexing gain
  • next-generation multiple access

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