
1. 中国电力科学研究院有限公司
2. 国网安徽省电力有限公司信息通信分公司
3. 国网安徽省电力有限公司
Published:2026
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项栩琛, 张慧, 陆俊, et al. 空天地一体化网络性能优化的最新进展、挑战以及研究前景[J]. 2026, 24(4): 33-43.
项栩琛, 张慧, 陆俊, et al. 空天地一体化网络性能优化的最新进展、挑战以及研究前景[J]. 2026, 24(4): 33-43. DOI: 10.16543/j.2095-641x.electric.power.ict.2026.04.05.
空天地一体化网络(space-air-ground integrated networks
SAGIN)通过天基网络、空基网络与地面无线网络的有机融合,成为推动6G无线通信网络发展的关键技术之一。该网络具备显著优势,能够将高速宽带服务覆盖至偏远及难以到达的区域。然而,受限于SAGIN自身结构的复杂性以及功率、存储等资源的有限性,其在实际运行中面临诸如多层动态优化、拓扑结构变化以及资源约束等关键挑战。基于人工智能(artificial intelligence
AI)的解决方案,如深度强化学习以及有监督与无监督学习方法,为应对上述挑战提供了具有前景的技术路径。通过引入AI技术,对网络拓扑、调度策略、资源分配、路由机制及移动性管理等核心要素进行协同优化,有助于提升SAGIN的决策与预测能力,从而增强其对外部复杂环境的适应性。对于充分挖掘SAGIN的潜在性能,并推动其在部署、配置与设计层面的最优实现具有重要意义。文章全面综述了SAGIN中关键性能要素的最新优化进展,深入分析了当前仍存在的技术瓶颈,并对未来可能的研究方向进行了展望。
缪巍巍,曾锃,张明轩,张厦千,张伯雷,钱柱中.基于多智能体强化学习的边缘物联代理资源分配算法[J].电力信息与通信技术,2021(12).
Liu Ziluan,Li Xin.Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks[J].The Journal of China Universities of Posts and Telecommunications,2018(01).
Yang Siyuan,Bouazizi Mondher,Ohtsuki Tomoaki,Shibata Yohei,Takabatake Wataru,Hoshino Kenji,Nagate Atsushi.Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System[J].Future Internet,2023.
Yan Peijie,Chu Feihuang,Jia Luliang,Qi Nan.A cross‐layer anti‐jamming method in satellite Internet[J].IET Communications,2022.
Nguyen Khoi Khac,Vien Ngo Anh,Nguyen Long D.,Le Minh Tuan,Hanzo Lajos,Duong Trung Q..Real-Time Energy Harvesting Aided Scheduling in UAV-Assisted D2D Networks Relying on Deep Reinforcement Learning[J].IEEE ACCESS,2021.
Yang Helin,Xie Xianzhong.Energy-Efficient Joint Scheduling and Resource Management for UAV-Enabled Multicell Networks[J].IEEE Systems Journal,2020.
Lei Lei,Eva Lagunas,Yaxiong Yuan,Mirza Golam Kibria,Symeon Chatzinotas,Bjorn Ottersten.Beam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping[J].IEEE Access,2020.
Lin Yun,Wang Meiyu,Zhou Xianglong,Ding Guoru,Mao Shiwen.Dynamic Spectrum Interaction of UAV Flight Formation Communication with Priority: A Deep Reinforcement Learning Approach[J].IEEE Transactions on Cognitive Communications and Networking,2020.
Han Chen,Huo Liangyu,Tong Xinhai,Wang Haichao,Liu Xian.Spatial Anti-jamming Scheme for Internet of Satellites Based on the Deep Reinforcement Learning and Stackelberg Game[J].IEEE Transactions on Vehicular Technology,2020.
Xin Hu,Shuaijun Liu,Yipeng Wang,Lexi Xu,Yuchen Zhang,Cheng Wang,Weidong Wang.Deep reinforcement learning-based beam Hopping algorithm in multibeam satellite systems[J].IET Communications,2019.
Mingxiang Guan,Zhou Wu,Yingjie Cui,Xuemei Cao,Le Wang,Jianfeng Ye,Bao Peng.An intelligent wireless channel allocation in HAPS 5G communication system based on reinforcement learning[J].EURASIP Journal on Wireless Communications and Networking,2019.
Nei Kato,Zubair Md. Fadlullah,Fengxiao Tang,Bomin Mao,Shigenori Tani,Atsushi Okamura,Jiajia Liu.Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence.[J].IEEE Wireless Commun.,2019.
Yuxia Bie,Longzi Wang,Ye Tian,Zhi Hu.A Combined Forecasting Model for Satellite Network Self-Similar Traffic.[J].IEEE Access,2019.
Hasan F. Ates,Syed Muhammad Hashir,Tuncer Baykas,Bahadir K. Gunturk.Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning.[J].IEEE Access,2019.
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