Aug 2026· IEEE Communications Standards Magazine· 0 citations· 15 references
Engineering
TL;DR
A multi-layer HAP-centric flying ad-hoc network (FANET) is proposed, highlighting HAP-centric FANETs as a foundation for resilient, scalable, and application-oriented 6G NTN deployments.
Abstract
High-altitude platforms (HAPs) are key enablers of next-generation non-terrestrial networks (NTNs), offering wide coverage, long endurance, and rapid deployment. Despite these advantages, current NTN designs remain satellite-centric and rely on terrestrial cellular assumptions, limiting flexibility and scalability. To overcome these limitations, this article proposes a multi-layer HAP-centric flying ad-hoc network (FANET). In this framework, HAPs are integrated with distributed uncrewed aerial vehicles (UAVs) to form a standalone, cell-free (CF) non-terrestrial system capable of autonomous operation. The layered architecture consists of an inter-HAP ad-hoc layer, a HAP-to-UAV cooperative layer, and a UAV-to-ground access layer, collectively enabling aerial connectivity, adaptive coverage, and interference-aware user access. Unique challenges for each layer are analyzed, including inter-HAP connectivity, FANET co-existence with terrestrial networks (TNs), and user access under heterogeneous conditions. Moreover, the article introduces enabling strategies such as fast beam alignment for high data rate connectivity, uncoordinated FANET/TN co-existence, and user localization and environment classification. Validated by three case studies, the discussion also outlines standardization pathways. The results highlight HAP-centric FANETs as a foundation for resilient, scalable, and application-oriented 6G NTN deployments.
Simulation results demonstrate that the proposed GNN-RSMA interference management algorithm outperforms conventional multiple access schemes while achieving fairness and worst-user performance comparable to successive convex approximation (SCA)-based optimization at only a fraction of its computational cost.
Afsoon Alidadi Shamsabadi, Animesh Yadav, H. Yanikomeroglu· 0 citations
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations
High altitude platform stations (HAPSs) are becoming a key component of future non-terrestrial networks (NTNs). HAPSs can serve a larger area than uncrewed aerial vehicles (UAVs) and offer lower propagation latency, maintenance expense, and energy costs than satellites. A major application of HAPSs is to serve the area...
Hao Lin, Mustafa A. Kishk, M. Alouini· IEEE Transactions on Wireles...· 0 citations
Timely and dependable information exchange is essential for large-scale unmanned aerial vehicle (UAV) swarms to coordinate under their fast motion, intermittent links, and limited energy on board. However, swarm deployments increasingly must contend with spectrum contention and jamming, as well as a lack of dependable...
Azzam Almekhlafi, Y. Alqudsi· 2026 6th International Confe...· 0 citations
Results show that TUAV-based NTN deployments can significantly outperform terrestrial 5G in per-user throughput, with the largest gains observed for cell-edge and low-SINR users, provided the TUAV altitude is properly chosen to balance improved line-of-sight probability against increased propagation loss and interferen...
RAMNA is introduced, a resource-aware optimizing algorithm designed to maximize network availability by autonomously repositioning UAVs at runtime, and contributes to the self-healing and self-optimization properties required for resilient, long-lived flying ad-hoc networks operating under energy uncertainty.
L. Pinto, Miguel Catarro, Alan Oliveira de Sá· SEAMS@ICSE· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.