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[2602.13200] Traffic Simulation in Ad Hoc Network of Flying UAVs with Generative AI Adaptation
Summary
This paper presents a model for traffic simulation in an Ad Hoc network of Unmanned Aerial Vehicles (UAVs) using generative AI to adapt communication channels, analyzing packet loss and transmission parameters.
Why It Matters
As UAV technology advances, optimizing communication in Ad Hoc networks is crucial for applications such as surveillance, delivery, and disaster response. This research leverages generative AI to enhance data transmission efficiency, addressing challenges in UAV networks.
Key Takeaways
- The study models traffic in Ad Hoc networks of UAVs, focusing on communication adaptation.
- Packet loss is analyzed based on various factors including packet size and transmission power.
- Generative AI is utilized to improve adaptive data transmission in UAV networks.
- The research provides program code for implementing the proposed model.
- Findings can inform future UAV communication systems and enhance operational efficiency.
Computer Science > Networking and Internet Architecture arXiv:2602.13200 (cs) [Submitted on 1 Jan 2026] Title:Traffic Simulation in Ad Hoc Network of Flying UAVs with Generative AI Adaptation Authors:Andrii Grekhov, Volodymyr Kharchenko, Vasyl Kondratiuk View a PDF of the paper titled Traffic Simulation in Ad Hoc Network of Flying UAVs with Generative AI Adaptation, by Andrii Grekhov and 2 other authors View PDF Abstract:The purpose of this paper is to model traffic in Ad Hoc network of Unmanned Aerial Vehicles and demonstrate a way for adapting communication channel using Artificial Intelligence. The modeling was based on the original model of Ad Hoc network including 20 Unmanned Aerial Vehicles. The dependences of packet loss on the packet size for different transmission powers, on the packet size for different frequencies, on Unmanned Aerial Vehicles flight area and on the number of Unmanned Aerial Vehicles were obtained and analyzed. The implementation of adaptive data transmission is presented in the program code. The dependences of packet loss, power and transaction size on time during Artificial Intelligence adaptation are shown. Comments: Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.13200 [cs.NI] (or arXiv:2602.13200v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2602.13200 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Andrii Grekhov Prof [view email] [v1] Th...
