Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 331-333· 0 citations· 11 references
Abstract
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.
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
: Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge, UAVs, and satellites; (2) a Reliability-Aware Multi-Objective Optimization Framework (RA-MOOF) that introduces explicit reliability guarantees through cross-layer link reliability modeling, node availability estimation, and smooth reliability proxy functions. Addressing the heterogeneous communication characteristics of the SAGIN architecture, this paper establishes a complete cross-layer delay model and composite reliability metrics. The reliability formulation is defined under explicitly stated conditional-independence assumptions, and the proposed smooth constraint terms are treated as surrogate CMDP costs rather than exact hard chance-constraint guarantees. Extensive experiments in a SAGIN simulation environment demonstrate that the proposed method improves the task completion rate by 3.8%, reduces average latency by 11.1%, and increases system reliability by 3.9% compared to state-of-the-art benchmarks. The optimization-only RA-Opt baseline is used as a non-real-time optimization reference for assessing reliability-aware offloading decision quality, while deployment-time decision-latency comparisons are interpreted primarily among learned inference policies. Comprehensive ablation studies and statistical validation across multiple random seeds confirm the contributions of each component, while cross-layer offloading decision analysis verifies the effectiveness of the method across different network layer selections.
Fei-Yan Bu, Zheng Wang, Yong Pan et al.· Computers, Materials & C...· 0 citations
The evolution of sixth-generation (6G) networks increasingly necessitates seamless and on-demand coverage across heterogeneous environments, particularly maritime regions where traditional terrestrial infrastructure is limited. In this paper, we aim to enhance the quality of service (QoS) for maritime users in the 6G space-air-sea integrated networks (SASINs). To shed light on the design of SASIN, we consider a communication model consisting of a single satellite, a single decode-and-forward (DF) uncrewed aerial vehicle (UAV) relay, and multiple maritime users. A novel on-demand coverage performance metric, service efficiency, is proposed to evaluate the QoS of maritime users. Particularly, in order to explore the boundary performance of the proposed architecture, both uplink and downlink communications are analyzed under the assumption of perfect channel state information (CSI). Furthermore, we formulate optimization problems to maximize the service efficiency for both uplink and downlink transmissions, subject to the user scheduling and decoding order, beamforming design, and placement of the relay UAV, respectively. To address the uplink optimization problems, we propose an alternating optimization (AO) algorithm that integrates a greedy randomized adaptive search procedure (GRASP)-based user scheduling algorithm with a successive convex approximation (SCA)-based UAV placement strategy to obtain a high-quality suboptimal solution. Analogously, for the downlink optimization problem, we develop an AO algorithm that combines a low-complexity greedy user scheduling scheme based on an initial beamforming design with the joint optimization of UAV placement and beamforming, effectively balancing performance and computational efficiency. Finally, extensive numerical results demonstrate that the proposed schemes achieve near-optimal performance with significantly reduced complexity, offering a strong solution for high-efficiency SASIN in future 6G maritime communications.
Yingqi He, Jinpeng Xu, Lin Zhou et al.· IEEE Transactions on Wireles...· 0 citations
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.
The rapid emergence of sixth-generation (6G) networks and the low-altitude economy has accelerated the evolution of wireless infrastructures toward air-ground integrated coverage networks (AGICNs), which seamlessly fuse terrestrial and aerial communication resources. However, existing AGICN studies primarily focus on coverage enhancement, while ignoring sustainability. Pursuing sustainable AGICNs introduces new challenges due to the multidimensional resource coupling across heterogeneous air-ground segments. In view of this, this paper presents a comprehensive survey and tutorial on sustainable AGICNs, aiming to balance coverage capacity with carbon efficiency in low-altitude economies. An integrated sensing, communication, and computation (ISCC)-driven architecture, which enables dynamic resource orchestration through closed-loop control, is proposed. We thus introduce a multi-dimensional sustainability metric system, which covers operational efficiency, task-oriented performance, and full lifecycle carbon emissions, to quantify energy and carbon footprints. We review enabling technologies, including artificial intelligence, hybrid precoding, integrated sensing and communication, and simultaneous wireless information and power transfer, and discuss their integration into the ISCC framework to minimize energy consumption while maintaining robust coverage. Experimental results on a real-world testbed demonstrate a 20% reduction in power consumption while achieving over 90% coverage probability, highlighting the feasibility of sustainable AGICNs for future green networks.
As the telecommunications industry advances towards the realisation of 6G, ubiquitous global coverage has emerged as a key objective. This has driven significant interest in the integration of terrestrial and non-terrestrial networks (ITNTNs), where satellite systems complement terrestrial infrastructure to enable seamless connectivity. However, the global operational scale of satellite systems necessitates cooperation between Low Earth Orbit satellite operators (LEOPs) and local mobile network operators (MNOs), introducing new economic and operational challenges. At the same time, emerging applications are expected to impose strict quality-of-service (QoS) requirements that must be guaranteed across both domains. This paper proposes a QoS-aware tiered pricing framework for supporting users with diverse QoS requirements in an ITNTN. Users are classified into service classes based on empirical traffic characterisation, with each class assigned a dedicated network slice and a target load level linked to QoS guarantees through a queueing-based latency model. The MNO determines class-specific prices that regulate aggregate demand to match these target loads. Numerical results demonstrate that the proposed framework enforces QoS requirements, preserves the desired load hierarchy, and aligns pricing with QoS differentiation, while remaining analytically tractable under heterogeneous user populations.
Simbarashe Tanyanyiwa, O. Falowo· International Conference on...· 0 citations
