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.
Abstract
Multi-tier aerial communication networks (MACNs), integrating satellites, high-altitude platforms, and unmanned aerial vehicles, are emerging as a cornerstone of next-generation global connectivity. Their promise of resilient and ubiquitous coverage, however, is hindered by highly dynamic topologies, severe energy and computational constraints, environment-sensitive channels, diverse quality-of-service requirements, and limited real-world validation. Artificial intelligence (AI) has increasingly been explored as a flexible framework to address these challenges, enabling adaptive routing, distributed computing and task offloading, handover management, intelligent resource allocation, and large-scale network optimization. This survey provides a comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives. We critically evaluate representative approaches in terms of scalability, efficiency, data demands, and practical deployability, and identify emerging trends such as graph neural networks with reinforcement learning for dynamic routing, predictive learning for mobility management, and federated learning for distributed computation. Persistent challenges remain in lightweight edge intelligence, real-world testbeds, reproducible benchmarking, and simulation-to-deployment transfer. To address these issues, we offer a research roadmap emphasizing compressible and interpretable models, standardized benchmarks, realistic validation, and hybrid designs that balance adaptability with computational and energy overhead. Finally, we identify open challenges and future research directions, offering insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
6G targets ultra-wide coverage together with ultra-low-latency and ultra-reliable services. To this end, Space-Air-Ground Integrated Networks (SAGINs), which integrate non-terrestrial networks (NTNs) with terrestrial networks (TNs), have emerged as a key candidate architecture. However, legacy resource management methods designed for terrestrial systems are difficult to apply directly due to high mobility and long propagation delays (and Doppler effects) of satellite/aerial platforms, dynamic topologies, and constrained onboard resources. In addition, under short-packet transmission (finite blocklength) regimes, QoS analysis must go beyond average-rate metrics and explicitly ensure latency and reliability simultaneously. This paper surveys resource management for SAGIN/TN-NTN integration through a three-axis taxonomy: (i) resource allocation/scheduling, (ii) mobility/dynamics, and (iii) statistical multi-QoS (latency-reliability) modeling. We compare representative works spanning optimization, graph deep reinforcement learning (Graph DRL), and finite-blocklength-based analyses. We also summarize virtualization/slicing and security/robustness as cross-cutting constraints, and highlight open research challenges.
Minjae Go, Woongsoo Na· International Conference on...· 0 citations
Major disasters such as earthquakes, floods, and wildfires can rapidly destroy terrestrial communication infrastructure, producing an extreme operating environment in which power, road, and network outages compound one another. Owing to their rapid deployability, flexible networking, and three-dimensional mobility, unmanned aerial vehicle (UAV) swarms are being studied as a flexible component of emergency communication systems. This paper reviews UAV swarm ad-hoc network communication technology for emergency scenarios. It examines the technical characteristics and applicability boundaries of three network architectures---flat, hierarchical clustering, and space-air-ground integrated---and surveys recent advances in routing and medium access, intelligent networking optimization, and transmission and security assurance. Particular attention is given to the reported performance and applicability of emerging approaches, including reinforcement-learning-based adaptive routing, decentralized federated learning, digital twins, and semantic communication, under highly dynamic and resource-constrained conditions. Drawing on studies of emergency routing, post-disaster data collection, semantic forwarding, and multi-layer coverage, the paper assesses current validation methods and outlines research directions in energy use, scalability, security, resilience, and standardization. Its contribution is a cross-layer comparison that relates architecture choices to protocol requirements, implementation costs, and validation maturity.
Yihang Ren, Huatao Zhu, Jie Zhang· International Journal of Eme...· 0 citations
The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 0 citations
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
High-altitude platforms (HAPs) are emerging as persistent middle-layer infrastructures for space-air-ground integrated networks (SAGINs), offering a favorable compromise among coverage, latency, endurance, and deployment flexibility. Their role, however, is evolving beyond communication relaying toward the joint provision of sensing, storage, communication, computing, and intelligence (S^2C^2I). This survey presents a unified HAP-centric perspective on S^2C^2I integration. We first review HAP fundamentals, platform categories, and their principal roles in SAGINs, including wide-area access, relaying, backhaul, edge service, low-altitude aerial coordination, and cross-layer orchestration. We then develop an integrated architecture spanning multi-plane connectivity, payload functional splits, and a cloud-edge-HAP space continuum with hierarchical data, control, computing, and storage loops. The enabling technologies are systematically examined, covering heterogeneous RF, millimeter-wave, terahertz, free-space optical, and hybrid links; sensing payloads and integrated sensing and communication; onboard computing; storage and caching; and AI-based orchestration. We further synthesize standardization progress, open software and datasets, testbeds, field evidence, and a four-level evaluation methodology ranging from component validation to mission-level effectiveness. An emergency-response case study demonstrates that joint S^2C^2I orchestration substantially improves conjunctive service availability while reducing feeder-link traffic. Finally, we identify research opportunities in agentic AI, trustworthy autonomy, goal-oriented semantic operation and digital twins, and sustainable, certifiable, and open HAP-native systems. The resulting synthesis provides a coherent roadmap from platform design to network-wide deployment.
