AI & ML
Learning Representative Port Subsets for Efficient Fluid Antenna Systems
arXiv math.IT0 views
arXiv:2610.33730v1 Announce Type: new Abstract: Fluid antenna systems (FASs) provide many candidate ports, but probing all ports for each channel realization incurs substantial online overhead. We study a complementary offline problem: compress the candidate-port space once and reuse the compressed set across future channel realizations. The method collects optimal ports from sampled training realizations, requiring only one scan of the candidate ports per realization. On a DeepMIMO 7-by-7 FAS with 49 candidate ports, 120 training realizations yield 27 representative ports on average while retaining 99.995% of the channel power gain achieved by exhaustive port selection.
Source:https://arxiv.org/abs/2610.33730PreviousA Proof of the Langberg-M\'edard ConjectureNextPartial Information Decomposition When Some Sources Are Known: A Source Chain Rule and a No-Go Theorem
Related posts
The Autonomous Edge — Issue #13: Cheaper Agents, Harder Governance (Week of November 2, 2026)
0dev.to AI

Plano Físico vs Plano Funcional en AWS Networking: cómo los arquitectos senior resuelven lo que otros no ven
0dev.to AWS
We built Origami Tutor: free paper-craft lessons you can turn and replay in 3D
0dev.to WebDev
From two authorized photos to a short AI rap video (consent-first workflow)
0dev.to WebDev
Check a File Hash in the Browser, Without Uploading the File
0dev.to WebDev
Your AI keys were leaked at a platform. A local guard you can paste catches the abuse before the invoice.
0dev.to Security