Autors: Baltiiski, P. R., Iliev, I. G., Kehaiov, B. R., Poulkov, V. K., Cooklev T.
Title: Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks
Keywords: Spectrum monitoring; Cloud based radio access networks; Spec

Abstract: Spectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency-time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data.

References

    Issue

    Wireless Personal Communications, vol. 87, issue 3, pp. 815–835, 2015, United States, SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA, DOI: 10.1007/s11277-015-2631-8

    Цитирания (Citation/s):
    1. 1. Hadi M., Lawey A., El-Gorashi T., Elmirghani J., Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks, IEEE Access, April 2020, P(99):1-1, DOI: 10.1109/ACCESS.2020.2992555, ISSN 21693536 - 2020 - в издания, индексирани в Scopus или Web of Science
    2. 2. Ivanov A., Feature Extraction in Local SpectrumSensing for Next Generation CognitiveRadios, Journal of Mobile Multimedia, January 2019, Vol: 15, Issue: 1 & 2 pp.111-140, DOI: 10.13052/jmm1550-4646.15126 - 2019 - в издания, индексирани в Scopus или Web of Science
    3. 3. Ying Z., Luo X., Lin X., Chen W., Visual Analytics for Electromagnetic Situation Awareness in Radio Monitoring and Management, IEEE Transactions on Visualization and Computer Graphics , Volume: 26, Issue: 1, Jan. 2020, pp.590 – 600, DOI: 10.1109/TVCG.2019.2934655 - 2019 - в издания, индексирани в Scopus или Web of Science
    4. Li M., Yang D., Lin J., Li M., Tang J., SpecWatch: A framework for adversarial spectrum monitoring with unknown statistics, Computer Networks, Volume 143, 9 October 2018, Pages 176-190, ISSN: 1389-1286, DOI: 10.1016/j.comnet.2018.07.018 - 2018 - в издания, индексирани в Scopus или Web of Science
    5. Zhang X., Chen J., A monitoring method based on ADS-B messages and terrestrial radio spectrum data fusion, Conference: 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), March 2019, DOI: 10.23919/URSIAP-RASC.2019.8738513 - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    6. Shawel, B.S., Hailemariam Woledegebre, D., Pollin, S., "Deep-learning based Cooperative Spectrum Prediction for Cognitive Networks", 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018, pp. 133-137, 2018, DOI: 10.1109/ICTC.2018.8539570. - 2018 - в издания, индексирани в Scopus или Web of Science
    7. Zafar, S., Hussain, R., Hussain, F., Jangsher, S., "Interplay between Big Spectrum Data and Mobile Internet of Things: Current solutions and future challenges", Computer Networks, vol. 163, 2019, DOI: 10.1016/j.comnet.2019.106879. - 2019 - в издания, индексирани в Scopus или Web of Science
    8. Zhou, F., Xie, H., Luo, X., Zhao, Y., Zhang, J., Wei, Q., Gu, F., "A Survey of Visualization for Radio Monitoring and Management", Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, vol. 32, no. 10, pp. 1569-1580, 2020, DOI: 10.3724/SP.J.1089.2020.18493. - 2020 - в издания, индексирани в Scopus или Web of Science
    9. Z Guizani, N Hamdi, "CRAN, H‐CRAN, and F‐RAN for 5G systems: Key capabilities and recent advances",International Journal of Network Management, ISSN:1099-1190,Volume 27, Issue 5 September/October 2017 - 2017 - в издания, индексирани в Scopus или Web of Science
    10. Helbet R. , Monda V., Bechet A., Bechet P.Low Cost System for Terrestrial Trunked Radio Signals Monitoring Based on Software Defined Radio Technology and Raspberry Pi 4, 2020 International Conference and Exposition on Electrical And Power Engineering (EPE),IEEE,2020,DOI: 10.1109/EPE50722.2020.9305536 - 2020 - в издания, индексирани в Scopus или Web of Science
    11. Weber C., Verfahren zur automatischen Spektralanalyse für die Optimierung drahtloser Kommunikation und Sensorik,KIT Scientific Publishing, ISBN978-3-7815-1014-7,2020 г. - 2020 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    12. Zichen He, Danian Li, Application of Big Data Technology in the Development of Network Radio and Television Station ,CONVERTER,ISSN: 0010-8189,Vol.7 2021 - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    13. Metawa N., Metawa S.,Internet Financial Risk Early Warning Based on Big Data Analysis,American Journal of Business and Operations Research,ISSN (Print) 2770-0216, Volume 3 , Issue 1, PP: 48-60 , 2021 - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    14. Helbet R.,Bechet P., Monda V.,Miclaus S, Bouleanu I.,Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring ,Sensors,,Volume 21 , Issue 9,2021, ISSN: 1424-8220 ,https://doi.org/10.3390/s21093160 - 2021 - в издания, индексирани в Scopus или Web of Science
    15. Yang, F., "A Protection Model of Citizen Personal Information Administrative Law Based on BD Analysis and Edge Computing", Wireless Communications and Mobile Computing, vol. 2022, 2022, DOI: 10.1155/2022/3037942 - 2022 - в издания, индексирани в Scopus или Web of Science
    16. Wu H., Dong R., Xu Q., Liu Z., Liang L.,FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction (2023) Micromachines, 14 (4), art. no. 794, DOI: 10.3390/mi14040794 - 2023 - в издания, индексирани в Scopus или Web of Science
    17. Somers B., Sayakkara A., Hayes D.R., Le-Khac N.-A.,Finding Forensic Artefacts in Long-Term Frequency Band Occupancy Measurements Using Statistics and Machine Learning,Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST,570 LNICST,2024,pp.227-248,DOI:10.1007/978-3-031-56580-9_14 - 2024 - в издания, индексирани в Scopus или Web of Science
    18. Saradhi Thommandru, V., Suma, T., Teena, M.O., (...), Thamaraikannan, P., Manikandan, S. ,Intelligent Optimization Framework for Future Communication Networks using Machine Learning,Data and Metadata 3,277 - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    19. Wijesekara, P.A.D.S.N.,Blockchain and Artificial Intelligence for Big Data Analytics in Networking: Leading-edge Frameworks,Journal of Engineering Science and Technology Review 17(3), pp. 125-143 - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science