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

Statistical Optimization of WARP Radio Board Parameters for Frugal Spectrum Estimation Using AR Model

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Accurate as well as an efficient Spectrum Estimation (SE) is the fundamental requirement of any practical wireless communication system for seamless connectivity. In this work an attempt has been undertaken to accurately estimate spectrum through Wireless Open Access Research Platform (WARP) Radio Board using Autoregressive (AR) signal modelling based on Time Series analysis. A sequential optimization technique of the baseband (BB) and radio frequency (RF) gain parameters defined as BB_TX (dB), BB_RX (dB), RF_TX (dB) and RF_RX (dB) of the Transmitter and Receiver radio board parameters of the WARP board has been adopted to achieve the frugal SE. The technique involves incremental improvements employing Yule Walker (YW) method using Levinson Durbin Algorithm (LDA) along with the concept of Confidence Interval (CI). The proposed statistical approach based on Regression Analysis for the design optimization of WARP board parameters has also been compared and established over the Akaike Information Criteria (AIC), Bayesian Information Criterion (BIC) and Minimum Description Length (MDL) approaches of the Machine Learning (ML) tools for the best model selection. The frugalities in terms of Data length, Lag order along with other radio gain parameters, received signal power and antenna distance have been achieved by exhaustive practical experimentation on the WARP radio boards. The proposed Statistical Analysis (SA) shows a significant reduction of Minimum Data Length (80%), Lag Order (96%) and RF/BB Transmitter Power (30%) compared to classical methods employing YW technique.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data Availability

It is hereby declared that the relevant data could be made available subject to copyright of the publisher.

References

  1. Arati, P., Chowdary, V. M., Chakraborty, D., Dutta, D., & Sharma, J. R. (2014). Customization of freeware GIS software for management of natural resources data for developmental planning a case study. International Journal of Open Information Technologies, 2(4), 25–29.

    Google Scholar 

  2. Arlinghaus, S. (2023). Practical handbook of curve fitting. Boca Raton: CRC Press.

    Book  Google Scholar 

  3. Blossey, R. (2023). The Poisson-Boltzmann equation: An introduction. Cham: Springer Nature.

    Book  Google Scholar 

  4. Cavanaugh, J. E., & Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews: Computational Statistics, 11(3), e1460.

    Article  MathSciNet  Google Scholar 

  5. Chakraborty, D., & Sanyal, S.K. (2020a). A comparative study of parametric spectrum estimation techniques for cognitive radio using testbed prototyping. ICCACCS.

  6. Chakraborty, D., & Sanyal, S. K. (2020). WARP test bed implementation of lag order and data length-optimized AR Spectrum estimation algorithm. Computational Advancement in Communication Circuits and Systems: Proceedings of ICCACCS, 2018, 191–204.

    Article  Google Scholar 

  7. Chakraborty, D., & Sanyal, S. K. (2021). Time-series data optimized AR/ARMA model for frugal spectrum estimation in Cognitive Radio. Physical Communication, 44, 101252.

    Article  Google Scholar 

  8. Chakraborty, D., & Sanyal, S. K. (2023). Testbed Implementation of a Scalable ARIMA Model for Spectrum Estimation in Cognitive Radio-A Null Hypothesis Approach. IETE Journal of Research, 69(7), 4165–4183.

    Article  Google Scholar 

  9. S. Chiang, J. Zito, V.R. Rao, & M. Vannucci, Time-series analysis. Statistical Methods in Epilepsy. (Chapman and Hall/CRC 2024), pp. 166–200

  10. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science, 7, e623.

    Article  Google Scholar 

  11. Elsayed, E. E., Hayal, M. R., Nurhidayat, I., Shah, M. A., Elfikky, A., Boghdady, A. I., Juraev, D. A., & Morsy, M. A. (2024). Coding techniques for diversity enhancement of dense wavelength division multiplexing MIMO-FSO fault protection protocols systems over atmospheric turbulence channels. IET Optoelectronics, 18(1–2), 11–31.

    Article  Google Scholar 

  12. Gupta, S., Saluja, K., Goyal, A., Vajpayee, A., & Tiwari, V. (2022). Comparing the performance of machine learning algorithms using estimated accuracy. Measurement: Sensors, 24, 100432.

    Google Scholar 

  13. Hocquette, C., Niskanen, A., Järvisalo, M., & Cropper, A. (2024). Learning MDL logic programs from noisy data. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10553–10561.

    Article  Google Scholar 

  14. Işığıçok, E., Öz, R., & Tarkun, S. (2020). Forecasting and technical comparison of inflation in Turkey with box-jenkins (ARIMA) models and the artificial neural network. International Journal of Energy Optimization and Engineering (IJEOE), 9(4), 84–103.

    Article  Google Scholar 

  15. Koçkaya, K., & Develi, I. (2020). Spectrum sensing in cognitive radio networks: Threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking, 2020(1), 255.

    Article  Google Scholar 

  16. Konar, A., & Sidiropoulos, N.D. (2015). Parametric frugal sensing of moving average power spectra. ICASSP, IEEE international conference on acoustics, speech and signal processing – proceedings, 3162–3166. https://doi.org/10.1109/ICASSP.2015.7178554.

  17. Mangan, N. M., Kutz, J. N., Brunton, S. L., & Proctor, J. L. (2017). Model selection for dynamical systems via sparse regression and information criteria. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473(2204), 20170009.

    Article  MathSciNet  Google Scholar 

  18. Mehanna, O., & Sidiropoulos, N. D. (2013). Frugal sensing: Wideband power spectrum sensing from few bits. IEEE Transactions on Signal Processing, 61(10), 2693–2703.

    Article  MathSciNet  Google Scholar 

  19. Mosavat-Jahromi, H., Li, Y., Cai, L., & Pan, J. (2021). Prediction and modeling of spectrum occupancy for dynamic spectrum access systems. IEEE Transactions on Cognitive Communications and Networking, 7(3), 715–728.

    Article  Google Scholar 

  20. Putranto, P., Desvasari, W., Daud, P., Wijayanto, Y.N., Mahmudin, D., Kurniadi, D.P., Rahman, A.N., Hardiati, S., Setiawan, A., & Darwis, F. (2019). Performance comparison of Blackman, Bartlett, Hanning, and Kaiser window for radar digital signal processing. 2019 4th International conference on information technology, information systems and electrical engineering (ICITISEE), 391–394.

  21. Samayoa, W. F., Crespo, M. L., Cicuttin, A., & Carrato, S. (2023). A survey on FPGA-based heterogeneous clusters architectures. IEEE Access, 11, 67679–67706.

    Article  Google Scholar 

  22. Seth, D., Chakraborty, D., Ghosal, P., & Sanyal, S.K. (2017). Brain computer interfacing: A spectrum estimation based neurophysiological signal interpretation. 2017 4th International conference on signal processing and integrated networks (SPIN), 534–539.

  23. Sheng, D., & Wang, D. (2024). Change-points analysis for generalized integer-valued autoregressive model via minimum description length principle. Applied Mathematical Modelling, 127, 193–216.

    Article  MathSciNet  Google Scholar 

  24. Smith, T.J., & Stevenson, K.J. (2004). Origin 7.5 OriginLab Corporation, One Roundhouse Plaza, Northampton, MA 01060. 1–800–969–7720. www. OriginLab. com. Suggested price 699.00(retail,singleuser), 489.00 (educational, single user). Contact company for other pricing options. ACS Publications.

  25. Wang, L., She, A., & Xie, Y. (2023). The dynamics analysis of Gompertz virus disease model under impulsive control. Scientific Reports, 13(1), 10180.

    Article  Google Scholar 

  26. Yerranna, H., Sabat, S.L., Sunil, D.K., Udgata, S.K. (2016). Real time performance evaluation of energy detection based spectrum sensing algorithm using warp board. 2016 International conference on advances in computing, communications and informatics (ICACCI), 2719–2723.

Download references

Acknowledgements

The authors deeply acknowledge the support from DST, Govt. of India for this work in the form of FIST 2007 Project on “Broadband Wireless Communications”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Debashis Chakraborty.

Ethics declarations

Conflict of interest

The relevant data set as well as documentation regarding the working board and set-up would be made available if required or demanded by readers.

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

Chakraborty, D., Sanyal, S.K. Statistical Optimization of WARP Radio Board Parameters for Frugal Spectrum Estimation Using AR Model. Wireless Pers Commun 138, 2447–2468 (2024). https://doi.org/10.1007/s11277-024-11608-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11608-z

Keywords