Abstract
This paper introduces a novel prediction method for spatio-temporal non-stationary channels between unmanned aerial vehicles (UAVs) and ground control vehicles, essential for the fast and accurate acquisition of channel state information (CSI) to support UAV applications in ultra-reliable and low-latency communication (URLLC). Specifically, an empirical mode decomposition (EMD)-empowered spatio-temporal attention neural network is proposed, referred to as EMD-STANN. The STANN sub-module within EMD-STANN is designed to capture the spatial correlation and temporal dependence of CSI. Furthermore, the EMD component is employed to handle the non-stationary and nonlinear dynamic characteristics of the UAV-to-ground control vehicle (U2V) channel, thereby enhancing the feature extraction and refinement capabilities of the STANN and improving the accuracy of CSI prediction. Additionally, we conducted a validation of the proposed EMD-STANN model across multiple datasets. The results indicated that EMD-STANN is capable of effectively adapting to diverse channel conditions and accurately predicting channel states. Compared to existing methods, EMD-STANN exhibited superior predictive performance, as indicated by its reduced root mean square error (RMSE) and mean absolute error (MAE) metrics. Specifically, EMD-STANN achieved a reduction of 24.66% in RMSE and 25.46% in MAE compared to the reference method under our simulation conditions. This improvement in prediction accuracy provides a solid foundation for the implementation of URLLC applications.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The datasets in this study have been stored on GitHub and can be accessed through the following link: https://github.com/zyq5258/UAV-non-stationary-channel. Any interested researcher can access the data while complying with the corresponding terms. Further information can be obtained by contacting the first author via email for assistance (zhangqiuyun@swust.edu.cn). The data of this study will be stored in the above link for a long time and updated regularly as needed.
References
Ranjha A, Javed MA, Srivastava G et al (2023) Intercell interference coordination for uav enabled urllc with perfect/imperfect csi using cognitive radio. IEEE Open J Commun Soc 4:197–208. https://doi.org/10.1109/ojcoms.2022.3232888
Ranjha A, Javed MA, Piran MJ, et al (2024) Toward facilitating power efficient urllc systems in uav networks under jittering. pp 3031–3041, https://doi.org/10.1109/TCE.2023.3305550
Ranjha A, Kaddoum G, Dev K (2022) Facilitating urllc in uav-assisted relay systems with multiple-mobile robots for 6g networks: A prospective of agriculture 4.0. IEEE Trans Indust Inf 18(7):4954–4965. https://doi.org/10.1109/TII.2021.3131608
Chen K, Wang Y, Fei Z, et al (2020) Power limited ultra-reliable and low-latency communication in uav-enabled iot networks. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp 1–6,https://doi.org/10.1109/WCNC45663.2020.9120565
Ranjha A, Kaddoum G (2021) Urllc facilitated by mobile uav relay and ris: A joint design of passive beamforming, blocklength, and uav positioning. IEEE Int Things J 8(6):4618–462. https://doi.org/10.1109/JIOT.2020.3027149
Cai Y, Jiang X, Liu M et al (2022) Resource allocation for urllc-oriented two-way uav relaying. IEEE Trans Veh Technol 71(3):3344–334. https://doi.org/10.1109/TVT.2022.3143174
Zou Z, Careem M, Dutta A, et al (2023) Joint spatio-temporal precoding for practical non-stationary wireless channels. IEEE Trans Commun pp 1–1. https://doi.org/10.1109/tcomm.2023.3241326
Jiang H, Cui M, Ng DWK et al (2022) Accurate channel prediction based on transformer: Making mobility negligible. IEEE J Select Areas in Commun 40(9):2717–2732. https://doi.org/10.1109/JSAC.2022.3191334
Chen S, Hu J, Shi Y et al (2020) A vision of c-v2x: Technologies, field testing, and challenges with chinese development. IEEE Int Things J 7(5):3872–3881
Luo C, Ji J, Wang Q et al (2018) Channel state information prediction for 5g wireless communications: A deep learning approach. IEEE Trans Netw Sci Eng 7(1):227–236
Peng F, Zhang S, Jiang Z et al (2023) A novel mobility induced channel prediction mechanism for vehicular communications. IEEE Trans Wire Commun 22(5):3488–3502. https://doi.org/10.1109/TWC.2022.3219052
Jiang W, Schotten HD (2019) Neural network-based fading channel prediction: A comprehensive overview. IEEE Access 7:118112–118124
Zhou T, Zhang H, Ai B et al (2022) Deep-learning-based spatial–temporal channel prediction for smart high-speed railway communication networks. IEEE Trans Wire Commun 21(7):5333–5345. https://doi.org/10.1109/twc.2021.3139384
Shao Y, Zhao MM, Li L, et al (2022) Deep learning based channel prediction for ofdm systems under double-selective fading channels. In: 2022 International Symposium on Wireless Communication Systems (ISWCS). IEEE, pp 1–6
Liu G, Hu Z, Wang L et al (2022) Spatio-temporal neural network for channel prediction in massive mimo-ofdm systems. IEEE Trans Commun 70(12):8003–8016. https://doi.org/10.1109/tcomm.2022.3215198
Wu C, Yi X, Zhu Y et al (2021) Channel prediction in high-mobility massive mimo: From spatio-temporal autoregression to deep learning. IEEE J Select Areas in Commun 39(7):1915–1930. https://doi.org/10.1109/JSAC.2021.3078503
Bian J, Wang CX, Gao X et al (2021) A general 3d non-stationary wireless channel model for 5g and beyond. IEEE Trans Wire Commun 20(5):3211–3224. https://doi.org/10.1109/twc.2020.3047973
Lyu Y, Liang C, Chen J, et al (2024) Channel measurements and analysis for fixed-wing uav-to-vehicle communications at 2.7 ghz in rural area. IEEE Antenna Wire Prop Lett pp 1–5. https://doi.org/10.1109/LAWP.2024.3453497
Chang H, Wang CX, Liu Y et al (2021) A novel nonstationary 6g uav-to-ground wireless channel model with 3-d arbitrary trajectory changes. IEEE Int Things J 8(12):9865–9877. https://doi.org/10.1109/JIOT.2020.3018479
Sun R, Cheng N, Li C et al (2024) Knowledge-driven deep learning paradigms for wireless network optimization in 6g. IEEE Netw 38(2):70–78. https://doi.org/10.1109/MNET.2024.3352257
Yu J, Liu X, Gao Y et al (2022) Deep learning for channel tracking in irs-assisted uav communication systems. IEEE Trans Wire Commun 21(9):7711–7722
Shehzad MK, Rose L, Assaad M (2019) Rnn-based twin channel predictors for csi acquisition in uav-assisted 5g+ networks. In: 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 1–6
Zhu Y, Dong X, Lu T (2019) An adaptive and parameter-free recurrent neural structure for wireless channel prediction. IEEE Trans Commun 67(11):8086–8096
Mattu SR, Theagarajan LN, Chockalingam A (2022) Deep channel prediction: A dnn framework for receiver design in time-varying fading channels. IEEE Trans Veh Technol 71(6):6439–6453
Varshney R, Gangal C, Sharique M et al (2023) Deep learning based wireless channel prediction: 5g scenario. Procedia Comput Sci 218:2626–2635
Wei Y, Zhao MM, Liu A et al (2022) Channel tracking and prediction for irs-aided wireless communications. IEEE Trans Wire Commun 22(1):563–579
Kulkarni A, Seetharam A, Ramesh A et al (2019) Deepchannel: Wireless channel quality prediction using deep learning. IEEE Trans Veh Technol 69(1):443–456
Jiang W, Schotten HD (2020) Deep learning for fading channel prediction. IEEE Open J Commun Soc 1:320–332
Xiong L, Zhang Z, Yao D (2022) A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network. Wire Netw pp 1–14
Zhang Y, Wang J, Sun J et al (2020) Cv-3dcnn: Complex-valued deep learning for csi prediction in fdd massive mimo systems. IEEE Wireless Commun Lett 10(2):266–270
Chu L, Burghal D, Neuman M, et al (2024) Context-conditioned spatio-temporal predictive learning for reliable v2v channel prediction arxiv:2409.09978
Ladosz P, Oh H, Zheng G et al (2020) Gaussian process based channel prediction for communication-relay uav in urban environments. IEEE Trans Aerospace and Electron Syst 56(1):313–325. https://doi.org/10.1109/TAES.2019.2917989
Khawaja W, Ozdemir O, Guvenc I (2021) Channel prediction for mmwave ground-to-air propagation under blockage. IEEE Antenna Wireless Prop Lett 20(8):1364–1368. https://doi.org/10.1109/LAWP.2021.3078268
Zhang Q, Yang T, Wu C et al (2023) A non-stationary channel prediction method for uav communication network with error compensation. Eng Appl Art Intell 123:106206
Pang M, Zhu Q, Wang CX et al (2023) Geometry-based stochastic probability models for the los and nlos paths of a2g channels under urban scenarios. IEEE Int Things J 10(3):2360–2372. https://doi.org/10.1109/JIOT.2022.3211524
Colpaert A, Cui Z, Vinogradov E et al (2024) 3d non-stationary channel measurement and analysis for mamimo-uav communications. IEEE Trans Veh Technol 73(5):6061–6072. https://doi.org/10.1109/TVT.2023.3340447
Lyu Y, Wang W, Chen P (2024) Fixed-wing uav based air-to-ground channel measurement and modeling at 2.7ghz in rural environment. IEEE Trans Antenna Prop pp 1–1. https://doi.org/10.1109/TAP.2024.3428337
Cheng X, Li Y, Wang CX et al (2020) A 3-d geometry-based stochastic model for unmanned aerial vehicle mimo ricean fading channels. IEEE Int Things J 7(9):8674–8687. https://doi.org/10.1109/JIOT.2020.2995707
Huang Z, Cheng X (2021) A general 3d space-time-frequency non-stationary model for 6g channels. IEEE Trans Wireless Commun 20(1):535–548. https://doi.org/10.1109/twc.2020.3026356
Chen T, Gao S, Zheng S et al (2023) Emd and vmd empowered deep learning for radio modulation recognition. IEEE Trans Cogn Commun Netw 9(1):43–57. https://doi.org/10.1109/TCCN.2022.3218694
Lin Z, Li M, Zheng Z et al (2020) Self-attention convlstm for spatiotemporal prediction. Proceed AAAI Conf Art Intell 34(7):11531–11538
Remcom (2023) Wireless insite. https://www.remcom.com/wireless-insite-em-propagation-software
Joo J, Park MC, Han DS et al (2019) Deep learning-based channel prediction in realistic vehicular communications. IEEE Access 7:27846–27858. https://doi.org/10.1109/ACCESS.2019.2901710
Zhang Y, Wu Y, Liu A et al (2021) Deep learning-based channel prediction for leo satellite massive mimo communication system. IEEE Wireless Commun Lett 10(8):1835–1839. https://doi.org/10.1109/LWC.2021.3083267
Beijing University of Posts and Telecommunications (2024) AI Air Interface Channel Data. https://doi.org/10.12448/3ch6-w717, Published by China Mobile
Acknowledgements
This work was supported in part by the Natural Science Foundation of Sichuan Province (Grant No. 2023NSFSC1373) and in part by the Sichuan Science and Technology Program (Grant No. 2024NSFSC0476).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study’s conception and design. Qiuyun Zhang played a key role by conceptualizing the study, designing experiments, analyzing data, and writing the initial manuscript. Qiumei Guo conducted the investigations and provided valuable resources for the study. She also participated in experimental design and data analysis. Hong Jiang provided overall guidance and support throughout the project, including overseeing the research process, as well as revising the manuscript. Xinfan Yin guided unmanned aerial vehicle simulation experiments and flight tests. Mushtaq Muhammad Umer assists with paper writing, proofreading, and formatting. Ying Luo provided partial funding and guidance. Chun Wu contributed to the visualization of the data. All authors reviewed and contributed to the final manuscript, providing critical feedback and revisions.
Corresponding author
Ethics declarations
Competing Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, Q., Guo, Q., Jiang, H. et al. EMD empowered neural network for predicting spatio-temporal non-stationary channel in UAV communications. Appl Intell 55, 285 (2025). https://doi.org/10.1007/s10489-024-06165-8
Accepted:
Published:
DOI: https://doi.org/10.1007/s10489-024-06165-8