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[2602.13199] Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks
Summary
This study explores AI-assisted channel adaptation in UAV-enabled cellular networks, focusing on the impact of adaptive channel control on communication performance under varying interference conditions.
Why It Matters
As UAV technology becomes increasingly integrated into cellular networks, understanding how AI can optimize communication channels is crucial for enhancing performance and reliability. This research contributes to the development of smarter, more efficient network systems that can adapt to real-time conditions, which is vital for applications in various sectors, including emergency services and logistics.
Key Takeaways
- The study employs a lightweight supervised machine learning approach for channel adaptation.
- Adaptive control of channel parameters significantly improves communication performance.
- The research highlights the importance of real-time adjustments based on packet-level performance indicators.
Computer Science > Networking and Internet Architecture arXiv:2602.13199 (cs) [Submitted on 1 Jan 2026] Title:Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks Authors:Andrii Grekhov, Volodymyr Kharchenko, Vasyl Kondratiuk View a PDF of the paper titled Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks, by Andrii Grekhov and 2 other authors View PDF Abstract:This paper presents a simulation based study of Artificial Intelligence assisted communication channel adaptation in Unmanned Aerial Vehicle enabled cellular networks. The considered system model includes communication channel Ground Base Station Aerial Repeater UAV Base Station Cluster of Cellular Network Users. The primary objective of the study is to investigate the impact of adaptive channel parameter control on communication performance under dynamically changing interference conditions. A lightweight supervised machine learning approach based on linear regression is employed to implement cognitive channel adaptation. The AI model operates on packet level performance indicators and enables real time adjustment of Transaction Size in response to variations in Bit Error Rate and effective Data Rate. A custom simulation environment is developed to generate training and testing datasets and to evaluate system behavior under both static and adaptive channel configurations. Comments: Subjects: Networking and Internet Architecture (cs.NI); Ar...
