Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks
This is an incremental study focusing on domain-specific optimization for UAV cellular networks.
This paper tackles the problem of improving communication performance in UAV-enabled cellular networks under dynamic interference by using a lightweight supervised machine learning approach for adaptive channel parameter control, resulting in real-time adjustments based on packet-level indicators.
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.