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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Enormous Fluid Antenna Systems (E-FAS) for Multiuser MIMO...
Farshad Rostami Ghadi, Kai-Kit Wong, Masoud Kaveh, Wee Kiat New, · 2026-02-12 · via cs.IT updates on arXiv.org

Enormous fluid antenna systems (E-FAS), the system concept that utilizes position reconfigurability in the large scale, have emerged as a new architectural paradigm where intelligent surfaces are repurposed from passive smart reflectors into multi-functional electromagnetic (EM) interfaces that can route guided surface waves over walls, ceilings, and building facades, as well as emit space waves to target receivers. This expanded functionality introduces a new mode of signal propagation, enabling new forms of wireless communication. In this paper, we provide an analytical performance characterization of an E-FAS-enabled wireless link. We first develop a physics-consistent end-to-end channel model that couples a surface-impedance wave formulation with small-scale fading on both the base station (BS)-surface and launcher-user segments. We illustrate that the resulting effective BS-user channel remains circularly symmetric complex Gaussian, with an enhanced average power that explicitly captures surface-wave attenuation and junction losses. For single-user cases with linear precoding, we derive the outage probability and ergodic capacity in closed forms, together with high signal-to-noise ratio (SNR) asymptotics that quantify the gain of E-FAS over purely space-wave propagation. For the multiuser case with zero-forcing (ZF) precoding, we derive the distribution of the signal-to-interference-plus-noise ratio (SINR) and obtain tractable approximations for the ergodic sum-rate, explicitly revealing how the E-FAS macro-gain interacts with the BS spatial degrees of freedom (DoF). In summary, our analysis shows that E-FAS preserves the diversity order dictated by small-scale fading while improving the coding gain enabled by cylindrical surface-wave propagation.