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
Sep 18
Robust Federated Q-Learning with Almost No Communication
The paper introduces Robust Fed-Q, a federated Q‑learning algorithm designed for settings where multiple agents interact with a shared Markov Decision Process and communicate through a central server. It combines model‑based and model‑free reinforcement learning techniques with a median‑of‑means strategy from robust statistics to handle a small fraction of adversarial agents. The authors prove that Robust Fed-Q achieves exact convergence to the optimal value function with high probability, attains near‑optimal finite‑time rates that benefit from collaboration, and requires only “~O(1)” communication rounds per guarantee.
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 Machine Learning
Aug 27
FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection
FedQoS is a federated learning framework that predicts future QoS failure probabilities for candidate access links in dynamic indoor‑outdoor environments, enabling reliable access‑node selection without centralizing user data. Each access node trains locally on its network logs, while a global QoS‑risk predictor is built through federated aggregation. Simulations using physics‑based synthetic datasets show that FedQoS reduces QoS‑failure rates compared to signal‑based and historical‑QoS heuristics, achieving near‑centralized performance even under non‑IID data conditions.
Hugging Face Trending Papers
Aug 26
FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection
Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobilit...
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
Sep 10
Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
arXiv:2609.08312v1 Announce Type: cross Abstract: To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) e...
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 AI
Sep 1
Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management
arXiv:2608.28878v1 Announce Type: cross Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrain...