Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks

TL;DR

This study uses simulation to evaluate how AI-driven adaptive channel control improves UAV-enabled cellular network performance amid interference, employing a lightweight machine learning model for real-time adjustments.

Contribution

It introduces a simulation framework and a linear regression-based AI method for real-time channel adaptation in UAV cellular networks, addressing dynamic interference challenges.

Findings

Adaptive channel control improves data rate and reduces errors.

AI-based adjustments outperform static configurations.

Real-time adaptation enhances network reliability.

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.