A Survey on Resource Management in Joint Communication and Computing-Embedded SAGIN
Not on the map. Why?
Abstract (arXiv)
The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As manufacturing advances and user demands evolve, space-air-ground integrated networks (SAGIN) with computational capabilities emerge as a viable solution for services requiring low latency and high computational power. Resource management within joint communication and computing-embedded SAGIN (JCC-SAGIN) presents greater complexity than traditional terrestrial networks. This complexity arises from the spatiotemporal dynamics of network topology and service demand, the interdependency of large-scale resource variables, and intricate tradeoffs among various performance metrics. Thus, a thorough examination of resource management strategies in JCC-SAGIN is crucial, emphasizing the role of non-terrestrial platforms with processing capabilities in 6G. This paper begins by reviewing the architecture, enabling technologies, and applications in JCC-SAGIN. Then, we offer a detailed overview of resource management modeling and optimization methods, encompassing both traditional optimization approaches and learning-based intelligent decision-making frameworks. Finally, we outline the prospective research directions in JCC-SAGIN.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Mar 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 3; 2025: 4; 2026: 2.
9 papers.
- 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
- 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
- 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
- 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
- 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
- 2025Constellation as a Service: Tailored Connectivity Management in Direct-Satellite-to-Device Networkscited by 0
- 2026Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Networkcited by 0
- 2026Space Generative AI with Solar Energy Harvestingcited by 0
- 2026Space Generative AI with Solar Energy Harvesting1 Sep 2026
- 2026Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network20 Jan 2026
- 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches19 Jul 2025
- 2025Constellation as a Service: Tailored Connectivity Management in Direct-Satellite-to-Device Networks1 Jul 2025
- 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference29 Mar 2025
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey13 Mar 2025
- 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks23 Dec 2024
- 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Survey9 Jul 2024
- 2024Large Language Models for UAVs: Current State and Pathways to the Future2 May 2024