arXiv AI
Jul 1
LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach
arXiv:2510. 10895v2 Announce Type: replace Abstract: Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured.
KISS: Keeping it Simple and Slotted when Learning to Communicate over Wireless
arXiv:2606. 00266v1 Announce Type: cross Abstract: A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access.
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arXiv Machine Learning
Jul 27
Variance-Reduced Q-Learning over Static and Time-Varying Networks
arXiv:2607. 21876v1 Announce Type: new Abstract: We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP).
arXiv Machine Learning
Jun 19
Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networks
arXiv:2605. 00457v4 Announce Type: replace-cross Abstract: The coexistence of NR-U and Wi-Fi in the unlicensed spectrum introduces a challenging resource management problem, where heterogeneous channel access mechanisms can lead to unbalanced spectrum utilization and severe Wi-Fi performance degradation.
arXiv Machine Learning
Jun 17
Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation
arXiv:2606. 10774v2 Announce Type: replace Abstract: Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations.
arXiv AI
Jun 30
Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints
arXiv:2606. 28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data.
arXiv Machine Learning
Jun 10
Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception
arXiv:2606. 10774v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information.
arXiv Machine Learning
Jun 16
Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
arXiv:2606. 16331v1 Announce Type: new Abstract: The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks.
arXiv Machine Learning
Jul 23
Temporal Fair Division in Multi-Agent Systems: From Precise Alternation Metrics to Scalable Coordination Proxies
arXiv:2605. 14879v2 Announce Type: replace-cross Abstract: Many intelligent computing and autonomous systems rely on multiple independent, often learning, agents repeatedly sharing a limited resource.
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.
arXiv AI
Jun 10
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
arXiv:2606. 09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost.
arXiv Machine Learning
Jul 16
Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
arXiv:2607. 13119v1 Announce Type: cross Abstract: In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration.
arXiv Machine Learning
Jul 21
Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).