arXiv AI

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.

arXiv Machine Learning

Jun 2

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.

By Kamil Szczech, Maksymilian Wojnar, Krzysztof Rusek, Katarzyna Kosek-Szott, Szymon Szott

agents benchmarks safety

arXiv Machine Learning

6d ago

Grounding Large Language Models as Generalizable Policies in Network Control

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.

By Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Chongbo Sun, Xuefeng Tao, Wei Yang, Le Zhang, Wenwu Zhu, Peng Cui, Zhi Wang

llms safety

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

llms agents reinforcement-learning fine-tuning

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.

By Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen, Chiapin Wang

reinforcement-learning efficiency benchmarks safety

arXiv AI

Jun 2

TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control

arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.

By Siqi Lai, Pan Zhang, Yuping Zhou, Jindong Han, Yansong Ning, Hao Liu

llms agents reinforcement-learning

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.

By Eslam Eldeeb, Hirley Alves

diffusion reinforcement-learning efficiency safety

arXiv AI

Jul 7

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 5

FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs

arXiv:2608. 03852v1 Announce Type: new Abstract: This paper proposes FedCritic-MIMO, a communication-efficient serverless federated multi-agent reinforcement learning framework for AI-native resource control across independently deployable cell-level controllers in open and disaggregated 6G RANs.

By Amin Farajzadeh, Melike Erol-Kantarci

agents reinforcement-learning efficiency benchmarks

arXiv AI

1d ago

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).

By Lin Yin, Tiejun Lv, Weicai Li, Xi Yu, Xiaoyu He

safety

arXiv AI

Jun 2

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

arXiv:2605. 18077v2 Announce Type: replace Abstract: Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information.

By Sangjun Bae, Yisak Park, Sanghyeon Lee, Seungyul Han

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 1

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.

By Tommaso Marzi, Cesare Alippi, Andrea Cini

agents reinforcement-learning benchmarks

arXiv AI

Jul 21

Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach

arXiv:2602. 01131v2 Announce Type: replace Abstract: With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming.

By Yue Zhong, Jiawen Kang, Yongju Tong, Hong-Ning Dai, Dong In Kim, Abbas Jamalipour, Shengli Xie

reinforcement-learning efficiency

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