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
With the rapid proliferation of high-bandwidth and low-latency services such as virtual reality (VR), holographic communications, and large-scale Internet of Things (IoT), the complexity of network resource management has increased significantly. Network resource optimization plays a crucial role in improving throughput, reducing latency, and enhancing energy efficiency by enabling efficient utilization of limited wireless and wired resources. Conventional approaches, including rule-based methods, mathematical optimization, and reinforcement learning-based techniques, can achieve satisfactory performance in specific environments. However, they suffer from limitations such as poor generalization to dynamic environments, high modeling complexity, and difficulties in real-time decision-making. To overcome these limitations, recent studies have begun to explore network resource optimization based on Large Language Models (LLMs). This paper presents a comprehensive survey of LLM-based network resource optimization techniques. Existing studies are classified according to the role of LLMs, and the characteristics and limitations of each approach are analyzed.
Junyoung Park, Woongsoo Na· International Conference on...· 0 citations