面向5G空中无线网络的AI无线传播建模与无线电环境地图
AI-Enabled Wireless Propagation Modeling and Radio Environment Maps for 5G Aerial Wireless Networks
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中文总结 AI 辅助
针对5G空中网络连接可靠性挑战，提出双阶段无线电环境地图框架，结合空间Transformer与门控循环单元解耦建模路径损耗与快衰落，在经验数据集上实现近3dB误差与超0.75空间相似性。
中文摘要 AI 辅助
随着空中移动应用的日益普及，其成功取决于地面与非地面网络连接的无缝集成。然而，由于地面基站（BSs）在视距（LoS）条件下对无人机（UAVs）产生的多小区干扰、天线旁瓣退化导致的覆盖空洞、局部多径衰落效应以及空中用户的高速动态特性，从地面电信网络提供可靠连接仍然具有挑战性。为建模这些通常因稀疏的真实世界数据而加剧的复杂性，本工作提出了一种双阶段无线电环境地图（REM）框架。我们的方法在物理上解耦了信道建模，其中空间Transformer首先锚定确定性的、大尺度的路径损耗几何结构，而门控循环单元（GRU）随后外推随机的、局部的快衰落偏差。通过将3D空间插值重构为1D径向序列预测任务，该框架在本质上与传播物理特性保持一致。我们使用经验5G数据集，将所提出的框架与最先进的基线方法（包括3D Kriging、UNet、Mamba和Inception）进行了评估。结果表明，在不同高度、用户动态和参考信号接收功率（RSRP）数据集上，站内泛化性能得到改善，信号强度预测误差接近3 dB，REM空间相似性指数超过0.75。最后，我们研究了REM和信道秩条件对UAV信道质量的影响，强调了可靠信道建模对于稳健空中连接的必要性。
英文摘要
With the gaining prominence of aerial mobility applications, their success depends on the seamless integration of terrestrial and non-terrestrial network connectivity. However, providing reliable connectivity from terrestrial telecommunication networks remains challenging due to multi-cell interference from base stations (BSs) under line-of-sight (LoS) conditions to unmanned aerial vehicles (UAVs), coverage holes caused by antenna sidelobe degradation, localized multipath fading effects, and the high-speed dynamics of aerial users. To model such complexities, often exacerbated by sparse real-world data, this work proposes a dual-stage radio environment map (REM) framework. Our approach physically decouples the channel modeling, where a spatial Transformer first anchors the deterministic, large-scale path loss geometry, while a gated recurrent unit (GRU) subsequently extrapolates the stochastic, localized fast- fading deviations. By reformulating 3D spatial interpolation as a 1D radial sequence prediction task, the framework inherently aligns with the physics of propagation. We evaluate the proposed framework against state-of-the-art baselines, including 3D Kriging, UNet, Mamba, and Inception, using empirical 5G datasets. The results demonstrate improved intra-site generalization across diverse altitudes, user dynamics, and reference signal received power (RSRP) datasets, achieving signal-strength predictions with errors near 3 dB and REM spatial-similarity indices exceeding 0.75. Finally, we examine the influence of REMs and channel rank conditions on UAV channel quality, underscoring the necessity of reliable channel modeling for robust aerial connectivity.
发表机构
- NC State University（北卡罗来纳州立大学）
- Polytechnique Montréal（蒙特利尔高等理工学院）
- Idaho National Laboratory（爱达荷国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
