Method of Optimal Test Statistic Search for Spectrum Sensing
DOI:
https://doi.org/10.20535/RADAP.2022.90.13-20Keywords:
transform, test statistic, radiomonitoring, spectrum shape, spectrum occupancyAbstract
The constant growth in the number of electronic devices leads to increasing of spectrum occupancy. In addition, new data transmission technologies are constantly used and time-frequency structure of signals become more complicate. These factors lead to a significant complication of electronic environment, which leads to new approaches to fast spectrum sensing.
In most publications, to make a decision about the presence or absence of signals in a given frequency band, some transform is taken from the signal, followed by the calculation of test statistics. However, it is not indicated for what reasons this type of test statistics was chosen and whether it is optimal for a given transform and type of signal spectrum shape.
Problem of choosing the optimal type of test statistics is especially actual when working in conditions of unknown and variable noise power, as well as with a wide dynamic range of signals. Test statistics should be sensitive to spectrum outliers. The essence of the proposed method is forming a set of test statistics and calculating the value of coefficient of efficiency as the sum of detection probabilities for different spectrum shapes using these statistics for different signal-to-noise ratios and spectrum occupancy. The maximum value of the coefficient of efficiency will correspond to the optimal type of test statistics.
As a result of the research, it was found that to separate frequency samples into signal and noise, it is advisable to use coefficient of variation. Prospects of further research in this direction should be focused on development of methods for dynamic transition between types of test statistics in process of radio monitoring, depending on changes in the electronic environment.
References
References
Captain K. M., Joshi M. V. (2022). Spectrum Sensing for Cognitive Radio. Fundamentals and Applications. CRC Press, 256 p.
Elmasry F. G. (2021). Dynamic Spectrum Access Decisions. Local, Distributed, Centralized, and Hybrid Designs. John Wiley & Sons Ltd., 728 p.
Liang Y.-C. (2020). Dynamic Spectrum Management. From Cognitive Radio to Blockchain and Artificial Intelligence. Springer, 180 р. doi: 10.1007/978-981-15-0776-2.
Du Ke-Lin, Swamy N. S. (2010). Wireless Communication Systems From RF Subsystems to 4G Enabling Technologies. Cambridge University Press, 1020 p.
Framework and overall objectives of the future development of IMT for 2020 and beyond. Recommendation. ITU-R M.2083-0. (2015). International Telecommunication Union, Geneva, 21 p.
Rembovsky А. М., Ashikhmin A. V., Kozmin V. A., Smolskiy S. M. (2018). Radio Monitoring: Automated Systems and Their Components. Springer, 486 p. doi:10.1007/978-3-319-74277-9.
Vartiainen J., Lehtomaki J. J. and Saarnisaari H. (2005). Double-threshold based narrowband signal extraction. 2005 IEEE 61st Vehicular Technology Conference, Vol. 2, pp. 1288-1292. doi: 10.1109/VETECS.2005.1543516.
Henttu P. and Aromaa S. (2002). Consecutive mean excision algorithm. IEEE Seventh International Symposium on Spread Spectrum Techniques and Applications, Vol.2, pp. 450-454. doi: 10.1109/ISSSTA.2002.1048582.
Vartiainen J., Lehtomäki J., Saarnisaari H., and Juntti M. (2010). Analysis of the Consecutive Mean Excision Algorithms. Journal of Electrical and Computer Engineering, Volume 2010, Article ID 459623, 13 p. doi:10.1155/2010/459623.
Vartiainen J. (2010). Concentrated signal extraction using consecutive mean excision algorithms. Dissertation. University of Oulu, Faculty of Technology, Department of Electrical and Information Engineering, 114 р.
Rembovskii A. M., Tokarev A. B. (2004). Avtomatizirovannii radiomonitoring na osnove odnokanalnoi i dvukhkanalnoi obrabotki dannikh [Automated radio monitoring based on single-channel and dual-channel data processing]. Vestnik MGTU [Bulletin of MSTU], No. 3(56), pp. 42-62. [In Rus.]
Bakker W. (2019). Automatic detection of outlandish signal behaviour in the spectrum of cellular networks. University of Twente, M. Sc. Thesis, The Netherlands, 68 р.
Buhaiov М. V. (2020). Iterative Method of Radiosignals Detection based on Decision Statistics. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, No. 81, pp. 11-20. DOI: 10.20535/RADAP.2020.81.11-20.
Rostami S., Arshad K., and Moessner K. (2012). Order-Statistic Based Spectrum Sensing for Cognitive Radio. IEEE Communications Letters, Vol. 16, Iss. 5., pp. 592-595. DOI: 10.1109/LCOMM.2012.030512.111887.
Jiang L., et al. (2019). Unilateral left-tail Anderson Darling test-based spectrum sensing with Laplacian noise. IET Communications, Vol. 13, Iss. 6, pp. 696-705. doi: 10.1049/iet-com.2018.5598.
Wang H., Yang E.-H., Zhao Z. and Zhang W. (2009). Spectrum sensing in cognitive radio using goodness of fit testing. IEEE Transactions on Wireless Communications, Vol. 8, No. 11, pp. 5427-5430. doi: 10.1109/TWC.2009.081586.
Zhang G., Wang X., Liang Y.-C. and Liu J. (2010). Fast and Robust Spectrum Sensing via Kolmogorov-Smirnov Test. IEEE Transactions on Communications, Vol. 58, No. 12, pp. 3410-3416. doi: 10.1109/TCOMM.2010.11.090209.
Kieu-Xuan T., Koo I. (2011). Cramer-von Mises test spectrum sensing for cognitive radio systems. Wireless Telecommunication Symposium, pp. 1-4. doi: 10.1109/WTS.2011.5960831.
Zhang J. (2002). Powerful goodness-of-fit tests based on the likelihood ratio. J. R. Statist. Soc., Vol. 64, Iss. 2, pp. 281-294. doi: 10.1111/1467-9868.00337.
Teguig D., Le Nir V. and Scheers B. (2014). Spectrum sensing method based on goodness of fit test using chi-square distribution. Electronics Letters, Vol. 50, Iss. 9, p. 713-715. doi:10.1049/el.2014.0302.
Teguig D., Le Nir V., Scheers B., and Horlin F. (2014). Spectrum Sensing Method Based on the Likelihood Ratio Goodness of Fit Test under Noise Uncertainty. International Journal of Engineering Research & Technology (IJERT), Vol. 3 Iss. 9, pp. 488-494.
Marques L., and Carvalho F. (2020). Cooperative Spectrum Sensing Based on Skewness Statistical Tests. XXXVIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais - SBrT 2020, Florianópolis, SC. doi:10.14209/SBRT.2020.1570660124.
Subekti A., Sugihartono, Rachmana N. S. and Suksmono A. B. (2014). A Cognitive Radio Spectrum Sensing Algorithm to Improve Energy Detection at Low SNR. Telkomnika, Vol. 12, No. 3, pp. 717-724. doi: 10.12928/TELKOMNIKA.v12i3.101.
Denkovski D., Atanasovski V., and Gavrilovska L. (2012). HOS Based Goodness-of-Fit Testing Signal Detection. IEEE Communications Letters, Vol. 16, Iss. 3, pp. 310-313. doi: 10.1109/LCOMM.2012.010512.111830.
Subekti A., Sugihartono, Rachmana N. S. and Suksmono A. B. (2014). A HOS based Spectrum Sensing for Cognitive Radio in Noise of Uncertain Power. 2nd International Conference on Information and Communication Technology (ICoICT), pp. 511-514. doi: 10.1109/ICoICT.2014.6914114.
Subekti A., Sugihartono, Rachmana N. S. and Suksmono A. B. (2014). A Jarque-Bera Test Based Spectrum Sensing for Cognitive Radio. 8th International Conference on Telecommunication Systems Services and Applications (TSSA), pp. 1-4. doi: 10.1109/TSSA.2014.7065944.
Lin F., et al. (2012). A Combination of Quickest Detection with Oracle Approximating Shrinkage Estimation and Its Application to Spectrum Sensing in Cognitive Radio. MILCOM 2012 - IEEE Military Communications Conference, pp. 1-6, doi: 10.1109/MILCOM.2012.6415682.
Zeng Y. and Liang Y.-C. (2009). Eigenvalue based Spectrum Sensing Algorithms for Cognitive Radio. IEEE Transactions on Communications, Vol. 57, Iss. 6, pp. 1784-1793. doi: 10.1109/TCOMM.2009.06.070402.
Lin F., et al. (2012). Generalized FMD Detection for Spectrum Sensing under Low Signal-to-Noise Ratio. IEEE Communications Letters, Vol. 16, Iss. 5, pp. 604-607. doi: 10.1109/LCOMM.2012.030512.112164.
Lin F., Qiu R. C. and Browning J. P. (2015). Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis. IEEE Transactions on Vehicular Technology, Vol. 64, Iss. 1, pp. 77-87. doi: 10.1109/TVT.2014.2321388.
Çiflikli C. and Ilgin F. Y. (2018). Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Technical Gazette, Vol. 25, No. 1, pp. 100-106. doi: 10.17559/TV-20161217120341.
Zeng Y. and Liang Y. (2007). Covariance Based Signal Detections for Cognitive Radio. 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 202-207. doi: 10.1109/DYSPAN.2007.33.
Chen J., Gibson A. and Zafar J. (2008). Cyclostationary spectrum detection in cognitive radios. IET Seminar on Cognitive Radio and Software Defined Radios: Technologies and Techniques, pp. 1-5. doi: 10.1049/ic:20080398.
Pattanayak S., Venkateswaran P., and Nandi R. (2018). Autocorrelation based spectrum sensing technique for cognitive radio application. EICE Communications Express, Vol. 7, Iss. 11, pp. 415-420. doi: 10.1587/comex.2018XBL0107.
Lundén J., Kassam S. A. and Koivunen V. (2010). Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio. IEEE Transactions on Signal Processing, Vol. 58, Iss. 1, pp. 38-52. doi:10.1109/TSP.2009.2029790.
Po K. and Takada J. (2007). Signal Detection Method based on Cyclostationarity for Cognitive Radio. Technical Report of IEICE, SR2007-38, pp. 109–114.
Zeng Y. and Liang Y. (2009). Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances. IEEE Transactions on Vehicular Technology, Vol. 58, Iss. 4, pp. 1804-1815. doi: 10.1109/TVT.2008.2005267.
Zhang Y. L., Zhang Q. Y. and Melodia T. (2010). A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks. IEEE Communications Letters, Vol. 14, Iss. 6, pp. 533-535. doi: 10.1109/LCOMM.2010.06.091954.
Bogale T. E., Vandendorpe L. and Le L. B. (2015). Wide-Band Sensing and Optimization for Cognitive Radio Networks With Noise Variance Uncertainty. IEEE Transactions on Communications, Vol. 63, Iss. 4, pp. 1091-1105. doi: 10.1109/TCOMM.2015.2394390.
Gautier M., Berg V. and Noguet D. (2012). Wideband frequency domain detection using Teager-Kaiser energy operator. 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), pp. 332-337. doi: 10.4108/icst.crowncom.2012.248336.
Bezruk V. M., Ivanenko S. A. (2018). Selection and recognition of the specified radio signals in the SW band. Information and Telecommunication Sciences, No. 2, pp. 21-26. DOI:10.20535/2411-2976.22018.21-26.
Moon K. T., Stirling W. C. (2000). Methematical Methods and Algorithms for Signal Processing. New Jersey: Prentice Hall Inc., 937 p.
Recommendation ITU-R SM.1600-3(09/2017). Technical identification of digital signals. International Telecommunication Union, Geneva, 25 p.
Kay S. M. (2013). Fundamentals of Statistical Signal Processing: Practical Algorithm Development. New York: Prentice Hall, 475 p.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 М. В. Бугайов
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).