Fluid Antenna Systems
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Abstract (arXiv)
Over the past decades, multiple antenna technologies have appeared in many different forms, most notably as multiple-input multiple-output (MIMO), to transform wireless communications for extraordinary diversity and multiplexing gains. The variety of technologies has been based on placing a number of antennas at fixed locations which dictates the fundamental limit on the achievable performance. By contrast, this paper envisages the scenario where the physical position of an antenna can be switched freely to one of the N positions over a fixed-length line space to pick up the strongest signal in the manner of traditional selection combining. We refer to this system as a fluid antenna system (FAS) for tremendous flexibility in its possible shape and position. The aim of this paper is to study the achievable performance of a single-antenna FAS system with a fixed length and N in arbitrarily correlated Rayleigh fading channels. Our contributions include exact and approximate closed-form expressions for the outage probability of FAS. We also derive an upper bound for the outage probability, from which it is shown that a single-antenna FAS given any arbitrarily small space can outperform an L-antenna maximum ratio combining (MRC) system if N is large enough. Our analysis also reveals the minimum required size of the FAS, and how large N is considered enough for the FAS to surpass MRC.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2020.
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Citing papers per year: 2025: 4; 2026: 3.
7 papers.
- 2025Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based?cited by 1
- 2025Deep Learning Optimization of Two-State Pinching Antennas Systemscited by 0
- 2025LNN-powered Fluid Antenna Multiple Accesscited by 0
- 2025Joint Channel Estimation and Computation Offloading in Fluid Antenna-assisted MEC Networkscited by 0
- 2026A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraintscited by 0
- 2026Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approachcited by 0
- 2026A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAScited by 0
- 2026A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAS28 May 2026
- 2026Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach15 Apr 2026
- 2026A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints17 Mar 2026
- 2025Joint Channel Estimation and Computation Offloading in Fluid Antenna-assisted MEC Networks16 Sep 2025
- 2025Deep Learning Optimization of Two-State Pinching Antennas Systems8 Jul 2025
- 2025LNN-powered Fluid Antenna Multiple Access30 Jun 2025
- 2025Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based?12 Feb 2025