arXiv AI

LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks

arXiv:2507. 17188v2 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers.

arXiv AI

Aug 6

Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks

arXiv:2608. 04590v1 Announce Type: new Abstract: The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity.

By Xiao Wang, Shun-Ren Yang

reinforcement-learning

arXiv Machine Learning

Aug 4

MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks

arXiv:2608. 01128v1 Announce Type: cross Abstract: In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.

By Yixin Zhang, Zhuohui Yao, Wenchi Cheng, Walid Saad

agents reinforcement-learning

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

arXiv AI

Jun 9

Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

arXiv:2510. 10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.

By Yang Li, Ruichen Zhang, Yinqiu Liu, Guangyuan Liu, Abbas Jamalipour, Xianbin Wang, Dong In Kim

llms nlp reinforcement-learning multimodal

arXiv AI

Jun 2

Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks

arXiv:2606. 01324v1 Announce Type: cross Abstract: The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensuring reliable connectivity for ground users deployment.

By Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas

agents reinforcement-learning

arXiv AI

Jun 6

X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks

arXiv:2606. 05262v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly considered as aerial platforms capable of providing both sensing and communication services, representing a promising paradigm for intelligent transportation systems.

By Remon Polus, Soumaya Cherkaoui

arXiv Machine Learning

Aug 7

Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment

arXiv:2603. 16141v2 Announce Type: replace-cross Abstract: Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical deployments must operate under partial observability and intermittent peer-to-peer connectivity.

By Enguang Fan, Yifan Chen, Zihan Shan, Klara Nahrstedt, Matthew Caesar, Jae Kim

agents reinforcement-learning

arXiv Machine Learning

Jul 15

Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning

arXiv:2607. 12590v1 Announce Type: cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment.

By Amber Srivastava

agents reinforcement-learning

arXiv Machine Learning

Jul 23

CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System

arXiv:2607. 19609v1 Announce Type: cross Abstract: In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user.

By Yi Yang, Qianqian Zhang, Huaxia Wang

reinforcement-learning benchmarks

arXiv AI

Jul 1

Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones

arXiv:2606. 30680v1 Announce Type: cross Abstract: Truck-drone delivery is an emerging last-mile logistics mode combining the long-haul capacity of trucks with the flexible service capability of drones.

By Xuanyu Liu, Hui Hu, Jiao Zhao, Ziliang Wang, Zhengbing He

reinforcement-learning robotics

arXiv Machine Learning

Jul 22

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.

By Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas

reinforcement-learning

arXiv AI

1d ago

An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).

By Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low

llms rag agents reinforcement-learning

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