Attentional Copula-Aided Turbo Fluid Antenna Massive Access

作者：Noor Waqar, Kai-Kit Wong, Chan-Byoung Chae, R. Murch · 发表于：IEEE Wireless Communications Letters · 年份：2026 · DOI：10.1109/lwc.2026.3665494 · 被引用次数：8 · 研究领域：Computer Science

Fluid antenna multiple access (FAMA) has recently emerged as a user-centric approach to massive connectivity. By exposing many reconfigurable “ports” on an antenna aperture, they exploit local spatial fading fluctuations at each user terminal (UT) to mitigate multiuser interference (MUI) without the need for precoding. While both turbo and fast FAMA promise ultra-massive access, its practicality is limited by the need to observe, at symbol rate, the complete set of per-port channel gains and received signals. This letter addresses this bottleneck with a novel plug-and-play attentional-copula extrapolator that reconstructs the joint per-symbol field of channel gains and received signals from a small subset of ports. The module couples monotone normalizing-flow marginals with a Transformer-based copula to capture both intra-port (channel-signal) and inter-port (spatial) dependencies, yielding drop-in estimates for existing turbo FAMA pipelines without retraining. Simulation results reveal that even with an extremely small port subset, the normalized mean-square error (NMSE) rapidly converges and the symbol error rate (SER) after turbo FAMA remains within a few percent of the full-channel state information (CSI) oracle across aperture sizes. By replacing exhaustive sampling with accurate learned extrapolation, the proposed scheme preserves turbo FAMA’s scalability while unlocking practical, low-overhead deployment—a viable pathway to extreme massive access.