Skip to main content
SpringerLink
Log in

Intelligent Channel Parameter Estimation System Based on Neural Network Regression Model

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

How to estimate channel parameters more effectively, intelligently and accurately is the key problem to realize the requirements of intelligent and adaptive short-wave communication system. Based on the detailed analysis of chirp signal and fractional Fourier transform, an intelligent channel parameter estimation system is constructed. By building and training the regression model of multilayer fully connected neural network, the estimation error of Doppler shift is reduced. The simulation results show that the hierarchical channel estimation algorithm improves the precision of channel parameter estimation and the anti-noise performance of the system.

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

Access this article

Log in via an institution

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

References

  1. Bergadà P, Deumal M, Vilella C, Regué J, Altadill D, Marsal S (2009) Remote sensing and Skywave digital communication from Antarctica[J]. Sensors 9(12):10136–10157

    Google Scholar 

  2. Huang H, Yang J, Huang H, Song Y, Gui G (2018) Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system[J]. IEEE Trans Veh Technol 67(9):8549–8560

    Google Scholar 

  3. Wang T, Zheng Z, Rehmani MH et al (2018) Privacy preservation in big data from the communication perspective—a survey[J]. IEEE Communications Surveys & Tutorials 21(1):753–778

    Google Scholar 

  4. Alsina-Pagès R, Hervás M, Orga F, Pijoan J, Badia D, Altadill D (2016) Physical layer definition for a long-haul HF Antarctica to Spain radio link[J]. Remote Sens 8(5):380

    Google Scholar 

  5. Alsina-Pagès RM, Salvador M, Hervás M et al (2015) Spread spectrum high performance techniques for a long haul high frequency link[J]. IET Commun 9(8):1048–1053

    Google Scholar 

  6. Pijoan JL, Altadill D, Torta JM et al (2014) Remote geophysical observatory in Antarctica with HF data transmission: a review[J]. Remote Sens 6(8):7233–7259

    Google Scholar 

  7. Lin Y, Wang M, Zhou X, et al. (2020) Dynamic Spectrum interaction of UAV flight formation communication with priority: a deep reinforcement learning approach[J]. IEEE Transactions on Cognitive Communications and Networking

  8. Liu M, Song T, Hu J et al (2018) Deep learning-inspired message passing algorithm for efficient resource allocation in cognitive radio networks[J]. IEEE Trans Veh Technol 68(1):641–653

    Google Scholar 

  9. Yihan X, Cheng X, Taining Z et al (2019) An intrusion detection model based on feature reduction and convolutional neural networks [J]. IEEE Access 7:42210–42219

    Google Scholar 

  10. Wang H, Guo L, Dou Z, Lin Y (2018) A new method of cognitive signal recognition based on hybrid information entropy and DS evidence theory[J]. Mobile Networks and Applications 23(4):677–685

    Google Scholar 

  11. Zheng S, Qi P, Chen S, Yang X (2019) Fusion methods for CNN-based automatic modulation classification[J]. IEEE Access 7:66496–66504

    Google Scholar 

  12. Wang M, Li Z, Huang D, Guo X (2018) Performance analysis of information fusion method based on bell function[J]. International Journal of Performability Engineering 14(4):729–740

    Google Scholar 

  13. Lin Y, Wang C, Wang J, Dou Z (2016) A novel dynamic spectrum access framework based on reinforcement learning for cognitive radio sensor networks[J]. Sensors 16(10):1675

    Google Scholar 

  14. Xue Z, Wang J, Ding G, Wu Q, Lin Y, Tsiftsis TA (2018) Device-to-device communications underlying UAV-supported social networking[J]. IEEE Access 6:34488–34502

    Google Scholar 

  15. Bland EC, McDonald AJ, de Larquier S et al (2014) Determination of ionospheric parameters in real time using SuperDARN HF radars[J]. J Geophys Res Space Physics 119(7):5830–5846

    Google Scholar 

  16. Thomas EG, Shepherd SG (2018) Statistical patterns of Ionospheric convection derived from mid-latitude, high-latitude, and polar SuperDARN HF radar observations[J]. J Geophys Res Space Physics 123(4):3196–3216

    Google Scholar 

  17. De Larquier S (2013) The mid-latitude ionosphere under quiet geomagnetic conditions: propagation analysis of SuperDARN radar observations from large ionospheric perturbations[D]. Virginia Tech

  18. Wagner LS, Goldstein JA, Rupar MA, Kennedy EJ (1995) Delay, Doppler, and amplitude characteristics of HF signals received over a 1300-km transauroral sky wave channel[J]. Radio Sci 30(3):659–676

    Google Scholar 

  19. Warrington EM, Stocker AJ (2003) Measurements of the Doppler and multipath spread of HF signals received over a path oriented along the mid-latitude trough[J]. Radio Sci 38(5):1080–1091

    Google Scholar 

  20. Warrington EM, Stocker AJ, Siddle DR (2006) Measurement and modeling of HF channel directional spread characteristics for northerly paths[J]. Radio Sci 41(02):1–13

    Google Scholar 

  21. Stocker AJ, Warrington EM (2011) The effect of solar activity on the Doppler and multipath spread of HF signals received over paths oriented along the midlatitude trough[J]. Radio Sci 46(01):1–11

    Google Scholar 

  22. Vilella C, Bergadà P, Deumal M, et al. (2006) Transceiver architecture and digital down converter design for a long distance, low power HF ionospheric link[C]. 10th IET International Conference on Ionospheric Radio Systems and Techniques (IRST 2006), 95–99

  23. Vilella C, Miralles D, Pijoan JL (2008) An Antarctica-to-Spain HF ionospheric radio link: sounding results[J]. Radio Sci 43(04):1–17

    Google Scholar 

  24. Ads AG, Bergadà P, Vilella C et al (2013) A comprehensive sounding of the ionospheric HF radio link from Antarctica to Spain[J]. Radio Sci 48(1):1–12

    Google Scholar 

  25. Shi C, Dou Z, Lin Y, Li W (2018) Dynamic threshold-setting for RF-powered cognitive radio networks in non-Gaussian noise[J]. Physical Communication 27:99–105

    Google Scholar 

  26. Hervás M, Alsina-Pagès R, Orga F, Altadill D, Pijoan J, Badia D (2015) Narrowband and wideband channel sounding of an Antarctica to Spain ionospheric radio link[J]. Remote Sens 7(9):11712–11730

    Google Scholar 

  27. Xianglin W, Yu B, Xie H, Song Z (2006) Pulse cumulating method for multi-targets’ joint azimuth-elevation frequency-delay estimation[J]. Journal of Electronics (China) 23(3):370–373

    Google Scholar 

  28. Li A, Wu Z, Lu H, Chen D, Sun G (2018) Collaborative self-regression method with nonlinear feature based on multi-task learning for image classification[J]. IEEE Access 6:43513–43525

    Google Scholar 

  29. Kirsanov EA (2008) An estimation of temporal parameters of signals sequence with pseudo-random frequency tuning at the output of digital panoramic radio receiving device[J]. Radioelectron Commun Syst 51(9):502–509

    Google Scholar 

  30. Sharma KK, Joshi SD (2007) Time delay estimation using fractional Fourier transform[J]. Signal Process 87(5):853–865

    MATH  Google Scholar 

  31. Yu J, Zhang L, Liu K, Liu D (2015) Separation and localization of multiple distributed wideband chirps using the fractional Fourier transform[J]. EURASIP J Wirel Commun Netw 2015(1):266

    Google Scholar 

  32. Staelin DH (1969) Fast folding algorithm for detection of periodic pulse trains[J]. Proc IEEE 57(4):724–725

    Google Scholar 

  33. Assous S, Hopper C, Lovell M, Gunn D, Jackson P, Rees J (2010) Short pulse multi-frequency phase-based time delay estimation[J]. The Journal of the Acoustical Society of America 127(1):309–315

    Google Scholar 

  34. Lin Y, Zhu X, Zheng Z, Dou Z, Zhou R (2019) The individual identification method of wireless device based on dimensionality reduction and machine learning[J]. J Supercomput 75(6):3010–3027

    Google Scholar 

  35. Zhou JT, Du J, Zhu H et al (2019) Anomalynet: an anomaly detection network for video surveillance[J]. IEEE Transactions on Information Forensics and Security 14(10):2537–2550

    Google Scholar 

  36. Riedinger R, Hong S, Norte RA, Slater JA, Shang J, Krause AG, Anant V, Aspelmeyer M, Gröblacher S (2016) Non-classical correlations between single photons and phonons from a mechanical oscillator[J]. Nature 530(7590):313–316

    Google Scholar 

  37. Zhang Z (2018) Optimization performance analysis of 1090ES ADS-B signal separation algorithm based on PCA and ICA[J]. International Journal of Performability Engineering 14(4):741–750

    Google Scholar 

  38. Adams DM (2013) Real-time auto tuning of a closed-loop second-order system with internal time-delay using pseudo-random binary sequences[J]. Dissertations & Theses - Gradworks 10:1552240

    Google Scholar 

  39. Liu S, Bai W, Liu G et al (2018) Parallel fractal compression method for big video data[J]. Complexity 2018:2016976

    MATH  Google Scholar 

  40. Liu S, Pan Z, Cheng X (2017) A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface[J]. Fractals 25(04):1740004

    Google Scholar 

  41. Tai N, Pan YJ, Yuan NC (2015) Quasi-coherent noise jamming to LFM radar based on Pseudo-random sequence phase-modulation[J]. Radioengineering 24(4):1013–1024

    Google Scholar 

  42. Liu S, Liu G, Zhou H (2019) A robust parallel object tracking method for illumination variations[J]. Mobile Networks and Applications 24(1):5–17

    Google Scholar 

  43. Zhou T, Li H, Zhu J, Xu C (2014) Subsample time delay estimation of chirp signals using FrFT[J]. Signal Process 96:110–117

    Google Scholar 

  44. Khan NA, Taj IA, Jaffri MN (2010) Instantaneous frequency estimation using fractional Fourier transform and Wigner distribution[C]. 2010 International Conference on Signal Acquisition and Processing. IEEE, 319–321

  45. Wang ZW, Wang XL, Xiang M, Liu JH, Zhang XA, Yang C (2012) Reservoir information extraction using a fractional fourier transform and a smooth pseudo Wigner-Ville distribution[J]. Appl Geophys 9(4):391–400

    Google Scholar 

  46. Tseng FM, Yu HC, Tzeng GH (2002) Combining neural network model with seasonal time series ARIMA model[J]. Technol Forecast Soc Chang 69(1):71–87

    Google Scholar 

  47. Xiao Z, Ye SJ, Zhong B, Sun CX (2009) BP neural network with rough set for short term load forecasting[J]. Expert Syst Appl 36(1):273–279

    Google Scholar 

  48. Gui G, Huang H, Song Y, Sari H (2018) Deep learning for an effective nonorthogonal multiple access scheme[J]. IEEE Trans Veh Technol 67(9):8440–8450

    Google Scholar 

  49. Jin W, Li Z J, Wei L S, et al. (2000) The improvements of BP neural network learning algorithm[C]. WCC 2000-ICSP 2000. 2000 5th international conference on signal processing proceedings. 16th world computer congress 2000. IEEE, 3: 1647–1649

Download references

Author information

Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Lantu Guo

  2. Harbin Engineering University, Harbin, China

    Yanan Liu & Wenxin Li

  3. China Research Institute of Radiowave Propagation, Qingdao, China

    Lantu Guo & Yanan Liu

  4. Shandong Aerospace Electronic Technology Research Institute, Yantai, China

    Wenxin Li

Authors
  1. Lantu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenxin Li.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, L., Liu, Y. & Li, W. Intelligent Channel Parameter Estimation System Based on Neural Network Regression Model. Mobile Netw Appl 25, 2291–2301 (2020). https://doi.org/10.1007/s11036-020-01612-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-020-01612-5

Keywords

Search

Navigation