arXiv:2604. 05175v2 Announce Type: replace-cross Abstract: We consider constrained ergodic resource optimization in wireless networks with graph-structured interference.
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.
arXiv:2607. 06833v1 Announce Type: new Abstract: Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network optimization.
arXiv:2606. 03794v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation that leverages the underlying graph structure of communication networks.
MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffi...
arXiv:2609.40170v1 Announce Type: new Abstract: MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple fr...
arXiv:2606. 07114v1 Announce Type: cross Abstract: Next-generation wireless networks, including satellite-to-Open RAN systems, demand agile and intelligent resource management capable of handling dynamic multi-user interference under stochastic quality of service constraints.
arXiv:2606. 00266v1 Announce Type: cross Abstract: A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access.
arXiv:2606. 17684v1 Announce Type: cross Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications.
arXiv:2606. 13848v1 Announce Type: cross Abstract: Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services.
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.
arXiv:2608. 08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks.
Oversmoothing is a fundamental limitation of deep graph neural networks (GNNs), where repeated message passing causes node representations to become increasingly similar, eventually collapsing toward a low-dimensional subspace. This phenomenon limits the effective depth of message-passing architectures and motivates the search for mechanisms that preserve representation diversity.