arXiv Machine Learning By Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression

Read the original on arXiv Machine Learning →

arXiv:2506. 18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users.

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arXiv AI
Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv Machine Learning
4d ago

Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning

This paper studies Restless Multi‑Armed Bandits with individual penalty constraints for dynamic wireless networks, allowing each arm to have distinct performance limits such as energy, activation, or age of information. It introduces the Penalty‑Optimal Whittle (POW) index, which depends only on an arm’s transition kernel and its constraints, making it computable offline and independent of system‑wide parameters. The authors prove the POW index policy is asymptotically optimal, present a deep reinforcement learning method to learn the index online, and show through simulations that it outperforms existing policies.

By Nida Zamir, I-Hong Hou