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arXiv Machine Learning By Xindi Tong, Chee Wei Tan, H. Vincent Poor

Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model

Read the original on arXiv Machine Learning →

The paper introduces the adversarial water-filling (AWF) framework for competitive resource allocation in frequency and space, particularly targeting multi-operator low Earth orbit satellite spectrum sharing. It presents theoretical results, algorithms, and a domain‑specific wireless foundation model that uses permutation‑invariant channel representations, a constraint‑aware graph neural network, and learned projected extragradient iterations to approximate stationary solutions of the minimax problem. Experiments show the model generalizes to unseen problem sizes and achieves over an order‑of‑magnitude speedup compared to Mirror‑Prox while maintaining comparable solution quality.

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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