Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Beam prediction and tracking mechanism with enhanced LSTM for mmWave aerial base station

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

By combining millimeter wave (mmWave) with abundant spectral resources and advanced directional beamforming technology, mmWave aerial base stations (mAeBSs) can provide high-speed services to users on the ground while reducing interference to existing terrestrial networks. Due to the mobility of users and the corresponding changes of other environmental factors, the beam misalignment between mAeBSs and users will pose a serious challenge to the stability and quality of communication link. Consequently, we propose schemes based on deep learning for beam prediction tracking in this paper. On the basis of received signals from both the present and previous beam training, we apply long short term memory (LSTM) module to realize the beam prediction. In order to enhance performance of LSTM scheme, cascaded LSTMs are designed in the model. Furthermore, we attain the derivative of implicit state function of the beam changes to formulate an ordinary differential equation (ODE) problem and construct the LSTM_ODE model for beam prediction. Simulations demonstrate that, in comparison to existing other schemes, our proposed approaches attain greater beamforming gain while incurring less overhead during beam training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. https://deepmimo.net/versions/v2-python/.

References

  1. Weng, L., Zhang, Y., Yang, Y., Fang, M., & Yu, Z. (2021). A mobility compensation method for drones in SG-eIoT. Digital Communications and Networks, 7(2), 196–200. https://doi.org/10.1016/j.dcan.2020.07.011

    Article  Google Scholar 

  2. Xiao, Z., Chen, Y., Jiang, H., Hu, Z., Lui, J. C., Min, G., & Dustdar, S. (2022). Resource management in UAV-assisted MEC: State-of-the-art and open challenges. Wireless Networks, 28, 3305–3322. https://doi.org/10.1007/s11276-022-03051-4

    Article  Google Scholar 

  3. He, Q., Tan, S., Chen, F., Xu, X., Qi, L., Hei, X., Jin, H,. & Yang, Y. (2023). EDIndex: Enabling fast data queries in edge storage systems. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 675–685). https://doi.org/10.1145/3539618.3591676

  4. Qi, L., Yang, Y., Zhou, X., Rafique, W., & Ma, J. (2022). Fast Anomaly Identification Based on Multiaspect Data Streams for Intelligent Intrusion Detection Toward Secure Industry 4.0. IEEE Transactions on Industrial Informatics, 18(9), 6503–6511. https://doi.org/10.1109/TII.2021.3139363

    Article  Google Scholar 

  5. Yang, Y., Yang, X., Heidari, M., Khan, M. A., Srivastava, G., Khosravi, M., & Qi, L. (2022). ASTREAM: Data-stream-driven scalable anomaly detection with accuracy guarantee in IIoT environment. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2022.3157730

    Article  Google Scholar 

  6. Deng, D., Li, X., Menon, V., Piran, M. J., Chen, H., & Jan, M. A. (2022). Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks. Digital Communications and Networks, 8(4), 415–421. https://doi.org/10.1016/j.dcan.2021.07.007

    Article  Google Scholar 

  7. Xiao, Z., Zhu, L., Liu, Y., Yi, P., Zhang, R., Xia, X. G., & Schober, R. (2022). A survey on millimeter-wave beamforming enabled UAV communications and networking. IEEE Communications Surveys & Tutorials, 24(1), 557–610. https://doi.org/10.1109/COMST.2021.3124512

    Article  Google Scholar 

  8. Ma, W., Qi, C., Zhang, Z., & Cheng, J. (2020). Sparse channel estimation and hybrid precoding using deep learning for millimeter wave massive MIMO. IEEE Transactions on Communications, 68(5), 2838–2849. https://doi.org/10.1109/TCOMM.2020.2974457

    Article  Google Scholar 

  9. Nguyen, B. C., Xuan, N. T., Manh, H. T., Thanh, H. L., & Hiep, P. T. (2022). Performance analysis for multi-RIS UAV NOMA MmWave communication systems. Wireless Network, 29(2), 761–773. https://doi.org/10.1007/s11276-022-03171-x

    Article  Google Scholar 

  10. Zhao, Y., Zhou, F., Feng, L., Li, W., & Yu, P. (2023). MADRL-based 3D deployment and user association of cooperative mmwave aerial base stations for capacity enhancement. Chinese Journal of Electronics, 32(2), 283–294.

    Article  Google Scholar 

  11. Wang, X., Kong, L., Kong, F., Qiu, F., Xia, M., Arnon, S., & Chen, G. (2018). Millimeter wave communication: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(3), 1616–1653. https://doi.org/10.1109/COMST.2018.2844322

    Article  Google Scholar 

  12. Zou, C., Li, X., Liu, X., & Zhang, M. (2021). 3D placement of unmanned aerial vehicles and partially overlapped channel assignment for throughput maximization. Digital Communications and Networks, 7(2), 214–222. https://doi.org/10.1016/j.dcan.2020.07.007

    Article  Google Scholar 

  13. Li, J., Sun, Y., Xiao, L., Zhou, S., & Sabharwal, A. (2018). How to mobilize Mmwave: A joint beam and channel tracking approach. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3624–3628).

  14. Noh, Song, Zoltowski, Michael D., & Love, David J. (2017). Multi-resolution codebook and adaptive beamforming sequence design for millimeter wave beam alignment. IEEE Transactions on Wireless Communications, 16(9), 5689–5701. https://doi.org/10.1109/TWC.2017.2713357

    Article  Google Scholar 

  15. Luo, X., Liu, W., & Wang, Z. (2019). Calibrated beam training for millimeter-wave massive MIMO systems. In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) (pp. 1–5).

  16. Zhang, C., Guo, D., & Fan, P. (2016). Tracking angles of departure and arrival in a mobile millimeter wave channel. In 2016 IEEE International Conference on Communications (ICC) (pp. 1–6).

  17. Va, V., Vikalo, H., & Heath, R.W. (2016). Beam tracking for mobile millimeter wave communication systems. In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 743–747).

  18. Xu, X., Jiang, Q., Zhang, P., Cao, X., Khosravi, M. R., Alex, L. T., Qi, L., & Dou, W. (2022). Game theory for distributed IoV task offloading with fuzzy neural network in edge computing. IEEE Transactions on Fuzzy Systems, 30(11), 4593–4604. https://doi.org/10.1109/TFUZZ.2022.3158000

    Article  Google Scholar 

  19. Xu, X., Fang, Z., Zhang, J., He, Q., Yu, D., Qi, L., & Dou, W. (2021). Edge content caching with deep spatiotemporal residual network for IoV in smart city. ACM Transactions on Sensor Networks (TOSN), 17(3), 33. https://doi.org/10.1145/3447032

    Article  Google Scholar 

  20. Xu, X., Tian, H., Zhang, X., Qi, L., He, Q., & Dou, W. (2022). DisCOV: Distributed COVID-19 detection on X-Ray images with edge-cloud collaboration. IEEE Transactions on Services Computing, 15(3), 1206–1219. https://doi.org/10.1109/TSC.2022.3142265

    Article  Google Scholar 

  21. Yang, Chao, Wang, Xuyu, & Mao, Shiwen. (2022). RFID-based 3D human pose tracking: A subject generalization approach. Digital Communications and Networks, 8(3), 278–288. https://doi.org/10.1016/j.dcan.2021.09.002

    Article  Google Scholar 

  22. Jia, Y., Liu, B., Dou, W., Xu, X., Zhou, X., Qi, L., & Yan, Z. (2022). CroApp: A CNN-based resource optimization approach in edge computing environment. IEEE Transactions on Industrial Informatics, 18(9), 6300–6307. https://doi.org/10.1109/TII.2022.3154473

    Article  Google Scholar 

  23. Wang, Y., Wang, J., Zhang, W., Zhan, Y., Guo, S., Zheng, Q., & Wang, X. (2022). A survey on deploying mobile deep learning applications: A systemic and technical perspective. Digital Communications and Networks, 8(1), 1–17. https://doi.org/10.1016/j.dcan.2021.06.001

    Article  Google Scholar 

  24. Zhang, W., & Zhang, W. (2018). Beam training and tracking efficiency analysis for UAV mmWave communication. In 2018 IEEE International Conference on Communication Systems (ICCS) (pp. 115–119).

  25. Zheng, L., Zhang, W., Zhang, C., Zhang, J., & He, C. (2022). Low-overhead beam alignment for high-speed UAV communications. In 2022 IEEE/CIC International Conference on Communications in China (ICCC) (pp 59–64).

  26. Shah, S.H.A., Sharma, M., & Rangan, S. (2020). LSTM-based multi-link prediction for mmWave and sub-THz wireless systems. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC) (pp. 1–6).

  27. Alkhateeb, A. DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications. Preprint at arxiv.org/abs/1902.06435

  28. Xiao, Z., Zhu, L., & Xia, X.-G. (2020). UAV communications with millimeter-wave beamforming: Potentials, scenarios, and challenges’’. China Communications, 17(9), 147–166. https://doi.org/10.23919/JCC.2020.09.012

    Article  Google Scholar 

  29. Chen, Kangjian, Qi, Chenhao, & Li, Geoffrey Ye. (2020). Two-step codeword design for millimeter wave massive MIMO systems with quantized phase shifters. IEEE Transactions on Signal Processing, 68, 170–180. https://doi.org/10.1109/TSP.2019.2959250

    Article  MathSciNet  ADS  Google Scholar 

  30. Ma, K., He, D., Sun, H., Wang, Z., & Chen, S. (2021). Deep learning assisted calibrated beam training for millimeter-wave communication systems. IEEE Transactions on Communications, 69(10), 6706–6721. https://doi.org/10.1109/TCOMM.2021.3098683

    Article  Google Scholar 

  31. Ma, K., Zhang, F., Tian, W., & Wang, Z. (2023). Continuous-time mmWave beam prediction with ODE-LSTM learning architecture. IEEE Wireless Communications Letters, 12(1), 187–191. https://doi.org/10.1109/LWC.2022.3221159

    Article  Google Scholar 

  32. Dai, H., Yu, J., Li, M., Wang, W., Liu, A. X., Ma, J., Qi, L., & Chen, G. (2023). Bloom filter with noisy coding framework for multi-set membership testing. IEEE Transactions on Knowledge and Data Engineering, 35(7), 6710–6724. https://doi.org/10.1109/TKDE.2022.3199646

    Article  Google Scholar 

Download references

Funding

This research was supported by the National Natural Science Foundation of China (No. 62201083).

Author information

Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jinli Zhang, Fanqin Zhou & Wenjing Li

  2. Beijing Research Institute, China Telecom Corporation Limited, Beijing, 102209, China

    Fei Qi

Authors
  1. Jinli Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fanqin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenjing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fanqin Zhou.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Zhou, F., Li, W. et al. Beam prediction and tracking mechanism with enhanced LSTM for mmWave aerial base station. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03673-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-024-03673-w

Keywords

Search

Navigation