Dynamic Time-Difference QoS Guarantee in Satellite–Terrestrial Integrated Networks: An Online Learning-Based Resource Scheduling Scheme

Engineering ›› 2025, Vol. 54 ›› Issue (11) : 127 -142. DOI: 10.1016/j.eng.2025.09.025

Article

Author information +

History +

Received	Accepted	Published
2024-12-06	2025-09-17	2025-10-15
Issue Date	Revised Date
2025-10-15	2025-07-16

PDF (2846KB)

Abstract

The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning. However, given the inherent volatility of network traffic, ensuring differentiated quality of service in highly dynamic networks remains a significant challenge. In this paper, we propose an online learning-based resource scheduling scheme for satellite–terrestrial integrated networks (STINs) aimed at providing on-demand services with minimal resource utilization. Specifically, we focus on: ① accurately characterizing the STIN channel, ② predicting resource demand with uncertainty guarantees, and ③ implementing mixed timescale resource scheduling. For the STIN channel, we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks. We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction, while introducing conformal prediction to mitigate uncertainties arising from burst traffic. Additionally, we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale. We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information. Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform, numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.

Keywords

Satellite–terrestrial integrated networks / Dynamic resource scheduling / Conformal prediction / Online convex optimization

Cite this article

Download citation ▾

Xiaohan Qin, Tianqi Zhang, Kai Yu, Xin Zhang, Haibo Zhou, Weihua Zhuang, Xuemin Shen. Dynamic Time-Difference QoS Guarantee in Satellite–Terrestrial Integrated Networks: An Online Learning-Based Resource Scheduling Scheme. Engineering, 2025, 54(11): 127-142 DOI:10.1016/j.eng.2025.09.025

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Zhou D, Sheng M, Li J, Han Z.Aerospace integrated networks innovation for empowering 6G: a survey and future challenges.IEEE Commun Surv Tutor 2023; 25(2):975-1019.

[2]	Xiao Z, Yang J, Mao T, Xu C, Zhang R, Han Z, et al.LEO satellite access network (LEO-SAN) towards 6G: challenges and approaches.IEEE Wirel Commun 2024; 31(2):89-96.

[3]	Pan G, Ye J, An J, Alouini MS.Latency versus reliability in LEO mega-constellations: terrestrial, aerial, or space relay?.IEEE Trans Mobile Comput 2023; 22(9):5330-5345.

[4]	Ma T, Qian B, Qin X, Liu X, Zhou H, Zhao L.Satellite–terrestrial integrated 6G: an ultra-dense LEO networking management architecture.IEEE Wirel Commun 2024; 31(1):62-69.

[5]	Cao X, Yang B, Shen Y, Yuen C, Zhang Y, Han Z, et al.Edge-assisted multi-layer offloading optimization of LEO satellite–terrestrial integrated networks.IEEE J Sel Areas Commun 2023; 41(2):381-398.

[6]	Qin X, Ma T, Tang Z, Zhang X, Zhou H, Zhao L.Service-aware resource orchestration in ultra-dense LEO satellite–terrestrial integrated 6G: a service function chain approach.IEEE Trans Wirel Commun 2023; 22(9):6003-6017.

[7]	Xu Q, Su Z, Lu R, Yu S.Ubiquitous transmission service: hierarchical wireless data rate provisioning in space–air–ocean integrated networks.IEEE Trans Wirel Commun 2022; 21(9):7821-7836.

[8]	Kawamoto Y, Takahashi M, Verma S, Kato N, Tsuji H, Miura A.Traffic prediction-based dynamic resource control strategy in HAPS-mounted MEC-assisted satellite communication systems.IEEE Internet Things J 2024; 11(8):13824-13836.

[9]	Yuan S, Sun Y, Peng M.Joint network function placement and routing optimization in dynamic software-defined satellite–terrestrial integrated networks.IEEE Trans Wirel Commun 2024; 23(5):5172-5186.

[10]	Li D, Liu X, Yin Z, Cheng N, Liu J.CWGAN-based channel modeling of convolutional autoencoder-aided SCMA for satellite–terrestrial communication.IEEE Internet Things J 2024; 11(22):36775-36785.

[11]	Zhang X, Wang Y, Qin X, Zhang Z, Zhou H, Shen X.Link-level performance analysis of DVB standards in ultra-dense LEO satellite–terrestrial networks. In: Proceedings of IEEE 99th Vehicular Technology Conference; 2024 Jun 24–27; Singapore, IEEE, New York City (2024)

[12]	Yu J, Liu X, Gao Y, Shen X.3D on and off-grid dynamic channel tracking for multiple UAVs and satellite communications.IEEE Trans Wirel Commun 2022; 21(6):3587-3604.

[13]	Chen Z, Zhang J, Min G, Ning Z, Li J.Traffic-aware lightweight hierarchical offloading towards adaptive slicing-enabled SAGIN.IEEE J Sel Areas Commun 2024; 42(12):3536-3550.

[14]	Cai Y, Cheng P, Chen Z, Ding M, Vucetic B, Li Y.Deep reinforcement learning for online resource allocation in network slicing.IEEE Trans Mobile Comput 2024; 23(6):7099-7116.

[15]	Tu H, Zhao L, Zhang Y, Zheng G, Feng C, Song S, et al.Deep reinforcement learning for optimization of RAN slicing relying on control- and user-plane separation.IEEE Internet Things J 2024; 11(5):8485-8498.

[16]	Cui Y, Huang X, He P, Wu D, Wang R.QoS guaranteed network slicing orchestration for Internet of Vehicles.IEEE Internet Things J 2022; 9(16):15215-15227.

[17]	Li Q, Wang Y, Sun G, Luo L, Yu H.Joint demand forecasting and network slice pricing for profit maximization in network slicing.IEEE Trans Netw Sci Eng 2024; 11(2):1496-1509.

[18]	Jiang W, Zhang Y, Han H, Huang Z, Li Q, Mu J.Mobile traffic prediction in consumer applications: a multimodal deep learning approach.IEEE Trans Consum Electron 2024; 70(1):3425-3435.

[19]	Lin YH, Li GH.A Bayesian deep learning framework for RUL prediction incorporating uncertainty quantification and calibration.IEEE Trans Industr Inform 2022; 18(10):7274-7284.

[20]	He M, Wu H, Zhou C, Hu S, Tang Z, Zhuang W.Digital twin-assisted robust and adaptive resource slicing in LEO satellite networks.Proceedings of IEEE Global Communications Conference; 2024 Dec 8–12; Cape Town, South Africa, IEEE, New York City 2024; 3261-3266.

[21]	Sachdeva R, Gakhar R, Awasthi S, Singh K, Pandey A, Parihar AS.Uncertainty and noise aware decision making for autonomous vehicles: a Bayesian approach.IEEE Trans Veh Technol 2025; 74(1):378-389.

[22]	Aboeleneen AE, Abdellatif AA, Erbad AM, Salem AM.ECP: error-aware, cost-effective and proactive network slicing framework.IEEE Open J Commun Soc 2024; 5:2567-2584.

[23]	Garrido LA, Dalgkitsis A, Ramantas K, Ksentini A, Verikoukis C.Resource demand prediction for network slices in 5G using ML enhanced with network models.IEEE Trans Veh Technol 2024; 73(8):11848-11861.

[24]	Park S, Cohen KM, Simeone O.Few-shot calibration of set predictors via meta-learned cross-validation-based conformal prediction.IEEE Trans Pattern Anal Mach Intell 2024; 46(1):280-291.

[25]	Cohen KM, Park S, Simeone O, Shitz SS.Calibrating AI models for wireless communications via conformal prediction.IEEE Trans Mach Learn Commun Netw 2023; 1:296-312.

[26]	Piao S, Huang R, Tsung F.CRULP: reliable RUL estimation inspired by conformal prediction.IEEE Trans Instrum Meas 2024; 74:3505411.

[27]	Lai J, Liu H, Xu G, Jiang W, Wang X, Jiang D.Joint computation offloading and resource allocation for LEO satellite networks using hierarchical multi-agent reinforcement learning.IEEE Trans Cogn Commun Netw 2025; 11(4):2554-2567.

[28]	Jiang W, Zhan Y, Fang X.Fuzzy neural network based access selection in satellite–terrestrial integrated networks.J Netw Comput Appl 2025; 236:104108.

[29]	Wang H, Bai Y, Xie X.Deep reinforcement learning based resource allocation in delay-tolerance-aware 5G industrial IoT systems.IEEE Trans Commun 2024; 72(1):209-221.

[30]	Hou Y, Zhang K, Liu X, Chuai G, Gao W, Chen X.An inter-slice RB leasing and association adjustment scheme in O-RAN.IEEE Trans Netw Serv Manag 2024; 21(1):402-417.

[31]	Huang T, Fang Z, Tang Q, Xie R, Chen T, Yu FR.Dual-timescales optimization of task scheduling and resource slicing in satellite–terrestrial edge computing networks.IEEE Trans Mobile Comput 2024; 23(12):14111-14126.

[32]	Liu Y, Ma T, Qin X, Zhou H, Shen XS.Reconfigurable RAN slicing for ultra-dense LEO satellite networks via DRL.IEEE Trans Cogn Commun Netw 2025; 11(1):566-580.

[33]	Wang J, Dong M, Liang B, Boudreau G.Periodic updates for constrained OCO with application to large-scale multi-antenna systems.IEEE Trans Mobile Comput 2023; 22(11):6705-6722.

[34]	Choi P, Ham D, Kim Y, Kwak J.VisionScaling: dynamic deep learning model and resource scaling in mobile vision applications.IEEE Internet Things J 2024; 11(9):15523-15539.

[35]	Chouayakh A, Destounis A.Towards no regret with no service outages in online resource allocation for edge computing.Proceedings of IEEE International Conference on Communications; 2022 May 16–20; Seoul, Republic of Korea, IEEE, New York City 2022; 4378-4383.

[36]	TR 38.811: Study on new radio (NR) to support non-terrestrial networks. Report. Valbonne: 3rd Generation Partnership Project; 2020 Oct.

[37]	Series P.Attenuation by atmospheric gases and related effects.Geneva: International Telecommunication Union Radiocommunication Sector; 2019.

[38]	TR 38.901: Study on channel model for frequencies from 0.5 to 100 GHz. Report. Valbonne: 3rd Generation Partnership Project; 2024 Apr.

[39]	Xue J, Yu K, Zhang T, Zhou H, Zhao L, Shen X.Cooperative deep reinforcement learning enabled power allocation for packet duplication URLLC in multi-connectivity vehicular networks.IEEE Trans Mobile Comput 2024; 23(8):8143-8157.

[40]	Li Z, Liu F, Yang W, Peng S, Zhou J.A survey of convolutional neural networks: analysis, applications, and prospects.IEEE Trans Neural Netw Learn Syst 2022; 33(12):6999-7019.

[41]	Zhang Y, Xiong R, He H, Pecht MG.Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries.IEEE Trans Veh Technol 2018; 67(7):5695-5705.

[42]	Du W, Wang Y, Qiao Y.Recurrent spatial–temporal attention network for action recognition in videos.IEEE Trans Image Process 2018; 27(3):1347-1360.

[43]	Xu C, Xie Y.Sequential predictive conformal inference for time series.Proceedings of the 40th International Conference on Machine Learning, 2023 Jul 23–29; Honolulu, HI, UAS, ML Research Press, New York City 2023; 38707-38727.

[44]	Liu X, Ma T, Tang Z, Qin X, Zhou H, Shen XS.UltraStar: a lightweight simulator of ultra-dense LEO satellite constellation networking for 6G.IEEE/CAA J Autom Sinica 2023; 10(3):632-645.

[45]	Kassing S, Bhattacherjee D, Saethre JE, Singla A.Exploring the“ Internet from space” with Hypatia.Proceedings of the ACM Internet Measurement Conference; 2020 Oct 27–29; online, Association for Computing Machinery, New York City 2020; 214-229.

[46]	Chen M, Miao Y, Gharavi H, Hu L, Humar I.Intelligent traffic adaptive resource allocation for edge computing-based 5G networks.IEEE Trans Cogn Commun Netw 2020; 6(2):499-508.

[47]	Xu W, Zio E.A general data-driven framework for remaining useful life estimation with uncertainty quantification using split conformal prediction.Proceedings of the 7th International Conference on System Reliability and Safety; 2023 Nov 22–24; Bologna, Italy, IEEE, New York City 2023; 67-74.