The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-altitude road test data is cost-prohibitive, often yielding only sparse samples per BS. This scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples. To overcome these obstacles, we introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning. The former reduces feature space complexity by leveraging communications expertise, while the latter enhances model generalizability through the integration of propagation models and distinct subnetworks that capture and aggregate the semantic representations of latent features. Experimental evaluation confirms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm. Real-network validations further attest to its reliability, achieving practical prediction accuracy with MAE errors at the 5dB level.

翻译：低空经济的扩张凸显了低空网络覆盖预测对于设计空中走廊的重要性。虽然精确的LANC预测依赖于基站的天线波束方向图，但这些方向图通常具有专有性且不易获取。基站的运行参数本质上包含波束信息，为数据驱动的低空覆盖预测提供了契机。然而，收集大规模低空路测数据成本高昂，通常每个基站仅能获得稀疏样本。这种数据稀缺性导致两个主要挑战：由于高维运行参数的变化有限，而低维采样位置却存在显著变化，导致特征采样不平衡；以及因数据样本不足导致模型泛化能力下降。为克服这些障碍，我们提出了一种双管齐下的策略，包括基于专家知识的特征压缩和解耦表征学习。前者通过利用通信专业知识降低特征空间复杂度，后者则通过整合传播模型和专门捕获并聚合潜在特征语义表征的独立子网络来增强模型泛化能力。实验评估证实了我们框架的有效性，与最佳基线算法相比误差降低了7%。实际网络验证进一步证明了其可靠性，在5dB级别的MAE误差下实现了实用的预测精度。