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

arXiv: https://arxiv.org/abs/2507.14186 · HTML: https://mlsanity.com/paper/2507.14186/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Information-theoretic analysis of generalization capability of learning algorithms](https://mlsanity.com/paper/1705.07809/) (2017)
- [Disentangled Representation Learning](https://mlsanity.com/paper/2211.11695/) (2022)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020)
- [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024)
- [Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling](https://mlsanity.com/paper/2111.12228/) (2021)
- [Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing](https://mlsanity.com/paper/2403.08323/) (2024)
- [A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling](https://mlsanity.com/paper/2303.02308/) (2023)
- [A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond](https://mlsanity.com/paper/2101.06610/) (2021)
- [Network-Connected UAV: 3D System Modeling and Coverage Performance Analysis](https://mlsanity.com/paper/1901.07887/) (2019)
- [UAV Corridor Coverage Analysis with Base Station Antenna Uptilt and Strongest Signal Association](https://mlsanity.com/paper/2403.18311/) (2024)

## Built on by (most cited first, 4 of 4)

- [U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach](https://mlsanity.com/paper/2603.06401/) (2026)
- [WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimization](https://mlsanity.com/paper/2608.16955/) (2026)
- [Rethinking Radiomap Blind Prediction with Limited Environment and Configuration Representations](https://mlsanity.com/paper/2609.11255/) (2026)
- [Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning](https://mlsanity.com/paper/2609.32836/) (2026)

## Abstract (arXiv)

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.