# A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

arXiv: https://arxiv.org/abs/2007.15088 · HTML: https://mlsanity.com/paper/2007.15088/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM](https://mlsanity.com/paper/2401.00685/) (2024)
- [Hierarchical and Collaborative LLM-Based Control for Multi-UAV Motion and Communication in Integrated Terrestrial and Non-Terrestrial Networks](https://mlsanity.com/paper/2506.06532/) (2025)
- [Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks](https://mlsanity.com/paper/2205.13958/) (2022)
- [Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network](https://mlsanity.com/paper/2405.01314/) (2024)
- [Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models](https://mlsanity.com/paper/2507.16204/) (2025)
- [FLSTRA: Federated Learning in Stratosphere](https://mlsanity.com/paper/2302.00163/) (2023)
- [AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty](https://mlsanity.com/paper/2305.00780/) (2023)
- [Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks](https://mlsanity.com/paper/2503.03391/) (2025)
- [Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks](https://mlsanity.com/paper/2510.19731/) (2025)
- [VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT](https://mlsanity.com/paper/2210.08132/) (2022)
- [AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks](https://mlsanity.com/paper/2508.00011/) (2025)
- [Queue-Aware Graph Reinforcement Learning for UAV-ISAC-Assisted Maritime Data Collection](https://mlsanity.com/paper/2607.00324/) (2026)

## Abstract (arXiv)

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).