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arXiv Machine Learning By Quan Zhou, Ming Zeng

Learning Representative Port Subsets for Efficient Fluid Antenna Systems

Read the original on arXiv Machine Learning →

The paper proposes a method to reduce the number of ports that need to be probed in fluid antenna systems by selecting a representative subset of ports offline. By collecting optimal ports from a set of training channel realizations, the authors show that only one scan of the candidate ports is required per new realization. In a 7×7 DeepMIMO fluid antenna system with 49 candidate ports, 120 training realizations produce an average of 27 representative ports while preserving 99.995% of the channel power gain that would be achieved by exhaustive port selection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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