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EMD empowered neural network for predicting spatio-temporal non-stationary channel in UAV communications

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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.

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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

  1. 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

    Article  MATH  Google Scholar 

  2. 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

  3. 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

  4. 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

  5. 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

    Article  MATH  Google Scholar 

  6. 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

    Article  MATH  Google Scholar 

  7. 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

  8. 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

  9. 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

    Article  MATH  Google Scholar 

  10. 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

    Article  MathSciNet  MATH  Google Scholar 

  11. 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

  12. Jiang W, Schotten HD (2019) Neural network-based fading channel prediction: A comprehensive overview. IEEE Access 7:118112–118124

    Article  Google Scholar 

  13. 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

    Article  MATH  Google Scholar 

  14. 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

  15. 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

  16. 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

    Article  MATH  Google Scholar 

  17. 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

  18. 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

  19. 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

  20. 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

    Article  MATH  Google Scholar 

  21. 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

    Article  MATH  Google Scholar 

  22. 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

  23. 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

    Article  MATH  Google Scholar 

  24. 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

    Article  MATH  Google Scholar 

  25. Varshney R, Gangal C, Sharique M et al (2023) Deep learning based wireless channel prediction: 5g scenario. Procedia Comput Sci 218:2626–2635

    Article  MATH  Google Scholar 

  26. 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

    Article  MATH  Google Scholar 

  27. 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

    Article  MATH  Google Scholar 

  28. Jiang W, Schotten HD (2020) Deep learning for fading channel prediction. IEEE Open J Commun Soc 1:320–332

    Article  MATH  Google Scholar 

  29. 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

  30. 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

    Article  MATH  Google Scholar 

  31. Chu L, Burghal D, Neuman M, et al (2024) Context-conditioned spatio-temporal predictive learning for reliable v2v channel prediction arxiv:2409.09978

  32. 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

    Article  Google Scholar 

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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

    Article  MATH  Google Scholar 

  40. 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

  41. Lin Z, Li M, Zheng Z et al (2020) Self-attention convlstm for spatiotemporal prediction. Proceed AAAI Conf Art Intell 34(7):11531–11538

    MATH  Google Scholar 

  42. Remcom (2023) Wireless insite. https://www.remcom.com/wireless-insite-em-propagation-software

  43. 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

    Article  MATH  Google Scholar 

  44. 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

  45. Beijing University of Posts and Telecommunications (2024) AI Air Interface Channel Data. https://doi.org/10.12448/3ch6-w717, Published by China Mobile

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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).

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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.

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Correspondence to Qiuyun Zhang.

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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

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