A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction

TL;DR

This paper proposes a novel framework combining expert knowledge and disentangled representation learning to improve low-altitude network coverage prediction, addressing data scarcity and generalizability issues, with demonstrated accuracy improvements.

Contribution

It introduces a dual strategy of feature compression and disentangled learning to enhance coverage prediction accuracy and robustness under limited data conditions.

Findings

Achieved 7% error reduction over baseline methods.

Validated framework's effectiveness with real-network tests.

Maintained practical prediction accuracy with MAE errors at 5dB.

Abstract

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.