The Potential of Multilayered Hierarchical Nonterrestrial Networks for 6G: A Comparative Analysis Among Networking Architectures
Not on the map. Why?
Abstract (arXiv)
6th generation (6G) communication research is currently focusing on non-terrestrial networks (NTNs) to promote ubiquitous and ultra-high-capacity global connectivity. Specifically, multi-layered hierarchical networks, i.e., the orchestration among different aerial/space platforms, including Low and High Altitude Platforms (LAPs and HAPs), and satellites co-operating at different altitudes, currently represent one the most attractive technological options to solve coverage and latency constraints associated with the NTN paradigm. However, there are still several issues to be resolved for proper network design. In this work, we evaluate the performance of different multi-layered non-terrestrial configurations, and provide guidelines on the optimal working point(s) for which it is possible to achieve a good compromise between improved system flexibility and network performance, with respect to a baseline standalone deployment.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 2
- 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networkscited by 1
- 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0
- 2026Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Designcited by 0
- 2026Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design1 May 2026
- 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning2 Feb 2026
- 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks22 Oct 2025
- 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees13 May 2025