Search arXivSearch

arXiv · 2607.21375

Electromagnetic-Aware Fluid Antenna Array

Abstract

Fluid antenna arrays (FAAs) offer a promising means of exploiting spatial degrees of freedom through adaptive port positioning. However, most existing communication models treat antenna ports as independent channel samples and therefore overlook the electromagnetic coupling that fundamentally governs compact apertures. This paper develops an electromagnetic-aware current-domain framework for planar FAAs. The proposed model integrates position-dependent multiport impedance, mutual coupling, radiated and accepted power, source-voltage feasibility, and channel variations into a unified baseband-compatible description. The framework is implementation-agnostic: the closed-form half-wave-dipole model adopted in this paper is only one instance and can be replaced by full-wave, measured, or surrogate impedance and embedded-pattern models. Building on this framework, we formulate two optimization-oriented design problems. The first addresses single-beam superdirective beamforming through the joint optimization of port currents and positions under sidelobe, current, voltage, and geometry constraints. The second maximizes the multi-user weighted sum rate via current-domain precoding and position optimization under accepted-power, current, voltage, and spacing constraints. In both cases, the electromagnetic model is not applied as an after-design correction, but is incorporated directly into tractable alternating algorithms with convex current or precoding subproblems and reduced-gradient geometry updates. Simulation results demonstrate that, when properly modeled, mutual coupling can be exploited as a valuable design resource, enabling lower sidelobes and persistent sum-rate gains over fixed-grid and random fluid-antenna benchmarks.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhentian Zhang, Yuanhui Wu, Hao Jiang, Zhen Chen, Zaichen Zhang. 2026-07-23. Electromagnetic-Aware Fluid Antenna Array. https://arxiv.org/abs/2607.21375

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

FlashSAM: Lightweight Vision Model for Multi-UAV Token Communication in Low-Latitude Wireless Networks

Token Communication (TokenCom) is a promising paradigm for low-altitude wireless networks, as it focuses on transmitting task-relevant core information, particularly in environments with uncertainty, noise, and stringent bandwidth constraints. However, existing TokenCom systems still face several challenges, including inefficient knowledge base construction, ineffective token encoding, and limited support for multi-user token sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-Unmanned Aerial Vehicle (UAV) Token Communication (LVM-MTC) system. First, we develop a lightweight Segment Anything Model (SAM), termed FlashSAM, which incorporates a set of lightweight convolutional modules to significantly reduce the number of model parameters. Building on FlashSAM, we construct a Lightweight Knowledge Base (LKB) to enable efficient object-level perception. Next, we design an Efficient Token Codec (ETC) based on the Masked Autoencoder (MAE) architecture. ETC improves compression efficiency at both the pixel and token levels, and provides lightweight token decoding tailored for resource-constrained UAVs. Furthermore, we propose a Multi-UAV Token Sharing (MTS) scheme for multi-UAV TokenCom. By measuring token similarity across UAVs, MTS consolidates similar tokens and transmits them through broadcast transmission, thereby further improving transmission efficiency. Finally, simulation results validate the feasibility and effectiveness of the proposed LVM-MTC system.

cs.IT

Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes

Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown strong performance for short codes, but their scalability to longer codes is limited by the quadratic memory and computational cost of attention. In this paper, we introduce the Mamba message-passing decoder (MMPD), an attention-free syndrome-based neural decoder for binary linear codes. MMPD retains the Tanner-graph structure of a message-passing decoder by performing local pairwise aggregation along variable-check edges. To enable efficient long-range information propagation, these local updates are combined with bidirectional Mamba state-space blocks. By avoiding dense attention matrices, MMPD scales more favorably for long codes in both memory and computation. Experiments on the (1056, 880) LDPC code show that MMPD achieves a 0.45 dB gain over the state-of-the-art CrossMPT decoder at a specified target bit error rate, while reducing memory consumption by a factor of 1.5. This reduction factor increases substantially for longer codes, demonstrating the applicability of MMPD to scalable neural decoding of practical long codes.

cs.IT

ICL-SEC: Iterative Cross-Layer Semantic Error Correction

Iterative decoding has been central to the success of modern channel coding, where reliability information is repeatedly exchanged across decoding components to approach fundamental performance limits. This paper brings the same principle to semantic error correction by proposing iterative cross-layer semantic error correction (ICL-SEC), a framework that closes the loop between physical-layer soft channel decoder and application-layer language-model-empowered semantic decoder. In the proposed framework, a soft-input soft-output channel decoder first produces bit-level posterior probabilities, from which word-level reliabilities are derived. Words deemed reliable are exposed to a masked language model as semantic context, while unreliable words are masked. The language model then produces contextual word likelihoods, which are leveraged to generate extrinsic bit-level priors and fed back to the channel decoder for the next iteration. This iterative refinement progressively expands the set of confidently recovered words. A key contribution is our Confirm prior-update rule: once a word is judged reliable, its bits are assigned deterministic priors with probability one in subsequent iterations, making the word fully resolved side information for both the channel decoder and the language model. This successive-confirmation mechanism prevents oscillatory unmask-mask behavior and yields a reliability interpretation consistent across layers. Simulations over text transmission demonstrates that ICL-SEC substantially outperforms both conventional channel decoding and non-iterative CL-SEC. In particular, the proposed Confirm scheme reduces the bit error rate by more than two orders of magnitude relative to non-iterative CL-SEC, while also significantly improving the other five performance metrics.

cs.IT