Port Selection for Fluid Antenna Systems

Zhi, Chai; Wong, Kai-Kit; Tong, Kin‐Fai; Chen, Yu; Zhang, Yangyang

doi:10.1109/lcomm.2022.3152451

Cited by 20 publications

(32 citation statements)

References 16 publications

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“…A more realistic scenario is when only a few ports are observed to reason and deduce the best port by exploiting the spatial correlation among the ports. In [144], this problem was addressed for single-user fluid antenna systems using learning-based methods. How these methods fare for FAMA is, however, not understood and should be properly investigated.…”

Section: E Port Selectionmentioning

confidence: 99%

MIMO Evolution Beyond 5G Through Reconfigurable Intelligent Surfaces and Fluid Antenna Systems

et al. 2022

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With massive deployment, multiple-inputmultiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we Manuscript

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Section: E Port Selectionmentioning

confidence: 99%

MIMO Evolution Beyond 5G Through Reconfigurable Intelligent Surfaces and Fluid Antenna Systems

et al. 2022

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“…In this letter, nevertheless, we assume full knowledge of {g (u,u) k } to simplify our discussion and will study this in future work. Note that such approach was explored for single-user fluid antenna systems in [22].…”

Section: Proposed Algorithmsmentioning

confidence: 99%

Fast Fluid Antenna Multiple Access Enabling Massive Connectivity

et al. 2023

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Massive connectivity over wireless channels relies on aggressive spectrum sharing techniques. Conventionally, this may be achieved by sophisticated signal processing and optimization of applying multiple antennas and/or complex multiuser decoding at each user terminal (UT). Different from previous methods, this letter proposes a radical approach for massive connectivity, which employs fluid antenna at each UT to exploit the interference null, created naturally by multipath propagation and the randomness of UT's data, on a symbol-by-symbol basis for multiple access. The proposed fast fluid antenna multiple access (f -FAMA) system adopts a large, distributed antenna array at the base station (BS) to transmit each UT's signal from each of the BS antennas and lets each UT overcome the interference on its own using its fluid antenna. Our main contribution is a technique that estimates the best port of fluid antenna for reception at every symbol instance. The proposed approach needs only cross-correlation calculations and single-user decoding at each UT and requires no precoding at the BS. Simulation results demonstrate that for a BS with 16 antennas supporting 16 co-channel users, a multiplexing gain of 14.87 is achieved even when the channel has a strong line-of-sight (LoS) and multipath is few. The multiplexing gain can also rise to 24.36 if a 30-antenna BS is serving 30 co-channel users.

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“…This is because s-FAMA generally requires a large number of switchable positions (or ports) to exploit the deep fades occurring in narrow regions but acquiring observations at all the ports is infeasible. Similar to [15], this letter leverages deep learning to infer the best port given only a few observations over the ports. Nonetheless, one fundamental difference is that [15] investigated a single-user system while this letter addresses the multiuser s-FAMA network.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to [15], this letter leverages deep learning to infer the best port given only a few observations over the ports. Nonetheless, one fundamental difference is that [15] investigated a single-user system while this letter addresses the multiuser s-FAMA network. In particular, [15] attempts to estimate the UE's channel envelopes of all the ports whereas this work's objective is to estimate the average signal-to-interference plus noise ratios (SINRs) over the ports.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, one fundamental difference is that [15] investigated a single-user system while this letter addresses the multiuser s-FAMA network. In particular, [15] attempts to estimate the UE's channel envelopes of all the ports whereas this work's objective is to estimate the average signal-to-interference plus noise ratios (SINRs) over the ports. Another major difference is that this letter uses a more accurate channel model to emulate the spatial correlation over the ports, showing more importance on the size than resolution of fluid antenna, contrary to the results in [15].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Enabled Slow Fluid Antenna Multiple Access

et al. 2023

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The increasing interest of fluid antenna systems is reinforced by an unprecedented way of achieving multiple access, by exploiting moments of deep fades in space. This phenomenon, referred to as fluid antenna multiple access (FAMA), allows the fluid antenna at each user to be switched to a location in space (i.e., port) where the sum-interference power collectively suffers from a deep fade, resulting in a decent signal reception without the need of complex signal processing. Nevertheless, selecting the best port is an arduous task, which requires a large number of channel observations to obtain the high performance gain. This letter aims to devise a low-complexity port selection scheme for FAMA where each user has a small number of port observations only. We assume slow FAMA (s-FAMA) so that the selected port remains unchanged until the channel conditions change. A deep learning approach is proposed to infer the signal-to-interference plus noise ratios (SINR) at all the available ports given only a small number of observations. The simulation results exhibit that the proposed scheme is able to attain significant reductions in outage probability, and improvements in multiplexing gain, from a relatively small number of available port observations, showing great potential for future multiple access technologies.

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Port Selection for Fluid Antenna Systems

Cited by 20 publications

References 16 publications

MIMO Evolution Beyond 5G Through Reconfigurable Intelligent Surfaces and Fluid Antenna Systems

MIMO Evolution Beyond 5G Through Reconfigurable Intelligent Surfaces and Fluid Antenna Systems

Fast Fluid Antenna Multiple Access Enabling Massive Connectivity

Deep Learning Enabled Slow Fluid Antenna Multiple Access

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