用AI动态调节无人机基站通信参数，提升干扰变化下的网络性能。
Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks
- 用线性回归模型基于包级指标实时调整传输大小。
- 在动态干扰下，自适应配置使误码率降低23%，数据速率提升18%。
- 适合研究无人机蜂窝网络智能调控的工程师与科研人员。
本文开展了一项基于仿真的研究，探讨人工智能辅助的无人飞行器（UAV）增强蜂窝网络中通信信道自适应机制。系统模型包含地面基站、空中中继、无人机基站及蜂窝网络用户集群。研究主要考察在动态干扰条件下，自适应信道参数控制对通信性能的影响。采用轻量级监督学习方法——基于线性回归的机器学习模型，实现认知式信道自适应。该AI模型以包级性能指标为输入，可实时调整传输大小，响应比特误码率（BER）和有效数据速率的变化。为生成训练与测试数据并评估系统行为，构建了定制化仿真环境，并在静态与自适应信道配置下进行对比测试。
原文摘要 · Abstract (English)
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.
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