A New Analytical Approximation of the Fluid Antenna System Channel

Khammassi, Malek; Kammoun, Abla; Alouini, Mohamed‐Slim

doi:10.48550/arxiv.2203.09318

Cited by 10 publications

(19 citation statements)

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“…is the estimate of the SINR at the k-th port. To achieve (11), our objective is to develop an approach to infer the SINR of the unobserved ports based on the SINR of the observed ports by utilizing the spatial correlation over the ports, which we will address in the next subsection.…”

Section: Low-complexity Deep Learning-based Port Selectionmentioning

confidence: 99%

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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Section: Low-complexity Deep Learning-based Port Selectionmentioning

confidence: 99%

Deep Learning Enabled Slow Fluid Antenna Multiple Access

et al. 2023

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“…Preliminary studies on MA-enabled communication systems have validated their performance gain over conventional FPA systems in various system setups [9], [10], [16]- [19]. In [9], the mechanical MA architecture and a field-response based channel model for single-MA systems were proposed, where the conditions under which the field-response based channel model becomes the well-known line-of-sight (LoS) channel, geometric channel, Rayleigh and Rician fading channel models were discussed.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], the port selection for FAS was studied for approaching the maximum SNR at Rx, where the machine learning method was used to capture the implicit channel correlation between closely-spaced antenna ports so as to reduce the number of port observations. Considering the inaccurate channel model adopted in [16]- [18], the authors in [19] proposed a new analytical approximation of the FAS channel, and the FAS channel model was shown to achieve a good performance on approximating the spatial correlation between the antenna ports, i.e., Jake's model.…”

Section: Introductionmentioning

confidence: 99%

Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization

Zhu¹,

Ma²,

Ning³

et al. 2023

Preprint

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Movable antenna (MA) is a promising technology to improve wireless communication performance by varying the antenna position in a given finite area at the transceivers to create more favorable channel conditions. In this paper, we investigate the MA-enhanced multiple-access channel (MAC) for the uplink transmission from multiple users each equipped with a single MA to a base station (BS) with a fixedposition antenna (FPA) array. A field-response based channel model is used to characterize the multi-path channel between the antenna array of the BS and each user's MA with a flexible position. To evaluate the MAC performance gain provided by MAs, we formulate an optimization problem for minimizing the total transmit power of users, subject to a minimum-achievable-rate requirement for each user, where the positions of MAs and the transmit powers of users, as well as the receive combining matrix at the BS are jointly optimized. To solve this non-convex optimization problem involving intricately coupled variables, we develop two algorithms based on zero-forcing (ZF) and minimum mean square error (MMSE) combining methods, respectively. Specifically, for each algorithm, the combining matrix of the BS and the total transmit power of users are expressed as a function of the MAs' position vectors, which are then optimized by using the gradient descent method in an iterative manner. It is shown that the proposed ZF-based and MMSE-based algorithms can converge to high-quality suboptimal solutions with low computational complexities. Simulation results demonstrate that the proposed solutions for MA-enhanced multiple access systems can significantly decrease the total transmit power of users as compared to conventional FPA systems under both perfect and imperfect field-response information.

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“…This is different from what we observe from the simulation results reported in this letter where the multiplexing gain can saturate before it reaches U even if N increases without bound. The main reason is that the channel model used in[18] was based on the generalized complex Gaussian model which tended to have higher diversity than it should[15]. The model (2) adopted in this letter in contrast provides a more accurate channel seen at each fluid antenna.…”

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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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A New Analytical Approximation of the Fluid Antenna System Channel

Cited by 10 publications

References 0 publications

Deep Learning Enabled Slow Fluid Antenna Multiple Access

Deep Learning Enabled Slow Fluid Antenna Multiple Access

Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization

Fast Fluid Antenna Multiple Access Enabling Massive Connectivity

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