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Generative AI for Advanced UAV Networking

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arxiv 2404.10556 v1 pith:YCLVCHQT submitted 2024-04-16 cs.NI eess.SP

classification cs.NIeess.SP
keywords networkingcommunicationadvanceddiscussframeworkgenerativeimportantsystems
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With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions

    cs.NI 2025-04 conditional novelty 4.0 of 10

    A survey that organizes low-altitude economy networks into a layered architecture and argues that multi-technology integration is the key enabler.

  2. Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning

    cs.AI 2025-04 reject novelty 4.0 of 10

    A Hungarian-plus-diffusion multi-agent RL controller with a Lyapunov energy queue assigns UAV rescue tasks to ground robots and airships, reporting lower latency and steadier energy use in simulation.

  3. DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

    cs.AI 2024-11 reject novelty 4.0 of 10

    A diffusion-model-enhanced MARL algorithm (GDM-MADDPG) combined with greedy path planning is proposed for DNN task assignment in UAV swarms, claiming lower AoI and better load balancing in simulation.

  4. BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction

    cs.IT 2025-01 reject novelty 3.0 of 10

    A BERT-inspired transformer is trained to reconstruct masked synthetic massive MIMO channel state information, with reported MSE far below simple linear and MLP baselines.

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