arXiv:2512. 05207v3 Announce Type: replace-cross Abstract: Virtual Network Embedding (VNE) is a key enabler of network slicing, yet most formulations assume that each Virtual Network Request (VNR) has a fixed topology.
arXiv:2609.37085v1 Announce Type: cross Abstract: Data-intensive services in the Computing Continuum must balance analytics quality, resource usage, and cost across heterogeneous nodes with limited a...
arXiv:2504. 18902v3 Announce Type: replace-cross Abstract: In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective management of Virtualized Network Functions (VNFs) is essential.
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.
arXiv:2606. 09825v1 Announce Type: cross Abstract: Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation.
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.
arXiv:2608. 00908v1 Announce Type: cross Abstract: Modern network policy control maps intent to sequential placement-control decisions.
arXiv:2609.08232v1 Announce Type: cross Abstract: Detailed routing remains a dominant runtime bottleneck in physical design due to increasing complexity of design rules. Modern routers can struggle t...
arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.
The paper introduces a foundation model–aided actor‑critic algorithm to boost multi‑agent reinforcement learning (MARL) for optimizing random access (RA) wireless networks. By embedding a foundation model into a consensus‑based decentralized MARL framework, the authors provide convergence analysis that matches conventional MARL under local reward exchanges and nonlinear value function approximations. Numerical experiments demonstrate that this FM‑based approach markedly accelerates MARL training for RA network optimization.
arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.
The paper introduces the first strategyproof deep reinforcement learning mechanism for online resource allocation, combining a monotonic neural network, a generalized critical payment rule, and training via RL or imitation. This design ensures truthful reporting and individual rationality while capturing nonlinear resource interactions beyond linear models. Experiments on a four‑resource stream show the neural rule attains 97.8% of an offline welfare upper bound, outperforming linear learners and first‑come, first‑served baselines, and imitation training achieves comparable performance 89 times faster than RL.
arXiv:2510. 10895v2 Announce Type: replace Abstract: Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured.