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<p>This paper introduces a novel prediction method for spatio-temporal non-stationary channels between unmanned aerial vehicles (UAVs) and ground control vehicles, essential for the fast and accurate acquisition of channel state information (CSI) to support UAV applications in ultra-reliable and low-latency communication (URLLC). Specifically, an empirical mode decomposition (EMD)-empowered spatio-temporal attention neural network is proposed, referred to as EMD-STANN. The STANN sub-module within EMD-STANN is designed to capture the spatial correlation and temporal dependence of CSI. Furthermore, the EMD component is employed to handle the non-stationary and nonlinear dynamic characteristics of the UAV-to-ground control vehicle (U2V) channel, thereby enhancing the feature extraction and refinement capabilities of the STANN and improving the accuracy of CSI prediction. Additionally, we conducted a validation of the proposed EMD-STANN model across multiple datasets. The results indicated that EMD-STANN is capable of effectively adapting to diverse channel conditions and accurately predicting channel states. Compared to existing methods, EMD-STANN exhibited superior predictive performance, as indicated by its reduced root mean square error (RMSE) and mean absolute error (MAE) metrics. Specifically, EMD-STANN achieved a reduction of 24.66% in RMSE and 25.46% in MAE compared to the reference method under our simulation conditions. This improvement in prediction accuracy provides a solid foundation for the implementation of URLLC applications.</p>

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EMD empowered neural network for predicting spatio-temporal non-stationary channel in UAV communications

  • 期刊论文

  • 发表日期：

    2025年1月9日

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  • Qiuyun Zhang,
  • Qiumei Guo,
  • Hong Jiang,
  • Xinfan Yin,
  • Muhammad Umer Mushtaq,
  • Ying Luo,
  • Chun Wu

摘要

This paper introduces a novel prediction method for spatio-temporal non-stationary channels between unmanned aerial vehicles (UAVs) and ground control vehicles, essential for the fast and accurate acquisition of channel state information (CSI) to support UAV applications in ultra-reliable and low-latency communication (URLLC). Specifically, an empirical mode decomposition (EMD)-empowered spatio-temporal attention neural network is proposed, referred to as EMD-STANN. The STANN sub-module within EMD-STANN is designed to capture the spatial correlation and temporal dependence of CSI. Furthermore, the EMD component is employed to handle the non-stationary and nonlinear dynamic characteristics of the UAV-to-ground control vehicle (U2V) channel, thereby enhancing the feature extraction and refinement capabilities of the STANN and improving the accuracy of CSI prediction. Additionally, we conducted a validation of the proposed EMD-STANN model across multiple datasets. The results indicated that EMD-STANN is capable of effectively adapting to diverse channel conditions and accurately predicting channel states. Compared to existing methods, EMD-STANN exhibited superior predictive performance, as indicated by its reduced root mean square error (RMSE) and mean absolute error (MAE) metrics. Specifically, EMD-STANN achieved a reduction of 24.66% in RMSE and 25.46% in MAE compared to the reference method under our simulation conditions. This improvement in prediction accuracy provides a solid foundation for the implementation of URLLC applications.