The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
Open accessAbstract
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).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C. K., & Zhang, J. C. (2014) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2014.2328098 · Cited by 8,106
Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming
Wu, Q., & Zhang, R. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2936025 · Cited by 4,566
Laser Beam Propagation through Random Media
Andrews, L. C., & Phillips, R. L. (2005) · SPIE eBooks · doi:10.1117/3.626196 · Cited by 4,473
Coding Metamaterials, Digital Metamaterials and Programmable Metamaterials
Cui, T. J., Qi, M. Q., Wan, X., Zhao, J., & Cheng, Q. (2014) · Light: Science & Applications · doi:10.1038/lsa.2014.99 · Cited by 3,801
Wireless Communications Through Reconfigurable Intelligent Surfaces
Basar, E., Di Renzo, M., De Rosny, J., Debbah, M., Alouini, M.-S., & Zhang, R. (2019) · IEEE Access · doi:10.1109/access.2019.2935192 · Cited by 3,394
The lithium-ion battery: State of the art and future perspectives
Zubi, G., Dufo-López, R., Carvalho, M., & Pasaoglu, G. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.002 · Cited by 2,563
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends
Ding, Z., Lei, X., Karagiannidis, G. K., Schober, R., Yuan, J., & Bhargava, V. K. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2725519 · Cited by 2,392
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,060
Optical Communication in Space: Challenges and Mitigation Techniques
Kaushal, H., & Kaddoum, G. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2603518 · Cited by 1,927
Cited by
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking
Xiao, Z., Zhu, L., Liu, Y., Yi, P., Zhang, R., Xia, X.-G., & Schober, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3124512 · Cited by 420
Herich, D., & Vaščák, J. (2024) · Drones · doi:10.3390/drones8020034 · Cited by 14
Metadata
- Authors
- Karabulut Kurt, G., Khoshkholgh, M. G., Alfattani, S., Ibrahim, A., Darwish, T. S. J., Alam, M. S., Yanikomeroglu, H., & Yongacoglu, A.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2021
- Volume
- 23
- Issue
- 2
- Pages
- 729-779
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3045597924
- Cited by
- 538
- References
- 114
- FWCI
- 44.09
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future. https://app.cochat.ai/papers/10.1109/comst.2021.3066905
- The paper
- Karabulut Kurt, G., Khoshkholgh, M. G., Alfattani, S., Ibrahim, A., Darwish, T. S. J., Alam, M. S., Yanikomeroglu, H., & Yongacoglu, A. (2021). A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2021.3066905
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.