Kalman-Takens filtering in communication systems
Main Article Content
Abstract
We apply a model-free predictor based on the Kalman Filter to signal-to-noise ratio (SNR) data
from a mobile communication system experiment. The experiment consist of collecting performance
indicators on a mobile device during the trajectory of a city bus. In particular, we analyze the SNR
measured by the mobile, which is collected every second via an application. Since some mechanisms in
a mobile network depend on the SNR, like power control and handoff processes, our results show that it
is possible to use prediction models to improve several procedures in mobile communications systems.
Article Details
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References
Ahmad, S., Reinhagen, R., Muppirisetty, L. S., and Wymeersch, H. Predictive resource allocation evaluation with real channel measurements. IEEE International Conference on Communications, jul 2017.
Alawieh, B., Assi, C., Mouftah, H., and Alazemi, H. An effective rate adaptation scheme for multihop wireless networks. In The IEEE symposium on Computers and Communications, pages 249–254. IEEE, June 2010.
Ali, K. B., Zarai, F., Khdhir, R., Obaidat, M. S., and Kamoun, L. Qos aware predictive radio resource management approach based on mih protocol. IEEE Systems Journal, 12(2):1862–1873,jun 2018.
Aronsson, D., Svensson, T., and Stemad, M.Performance Evaluation of Memory-less and Kalman-based Channel Estimation for OFDMA.
In 2009 IEEE VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-5, pages 2314+. IEEE, 2009. Series Title: IEEE Vehicular Technology Conference VTC.
Atawia, R., Abou-Zeid, H., Hassanein, H. S., and Noureldin, A. Joint chance-constrained predictive resource allocation for energy-efficient video streaming. IEEE Journal on Selected Areas in Communications, 34(5):1389–1404, may 2016.
Auger, F., Hilairet, M., Guerrero, J. M., Monmasson, E., Orlowska-Kowalska, T., and Katsura, S. Industrial Applications of the Kalman Filter: A Review. IEEE Transactions on Industrial Electronics, 60(12):5458–5471, Dec. 2013.
Elsherbiny, H., Nagib, A. M., and Hassanein, H. S. 4G LTE User Equipment Measurements along Kingston Transit 502 Bus Route, 2020.
Hamilton, F., Berry, T., and Sauer, T. Ensemble kalman filtering without a model. Physical Review X, 6(011021):11021, mar 2016.
Hamilton, F., Lloyd, A. L., and Flores, K. B. Hybrid modeling and prediction of dynamical systems. PLoS Computational Biology, 13(7):e1005655, 2017.
Julier, S. J. and Uhlmann, J. K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3):401–422, 2004.
Julier, S. J., Uhlmann, J. K., and Durrant-Whyte, H. F. A new approach for filtering nonlinear systems. In Proceedings of 1995 American Con-
trol Conference-ACC’95, volume 3, pages 1628–1632, Seatle, WA, USA, 1995. IEEE.
Kalman, R. E. A new approach to linear filtering and prediction problems. Journal of basic Engineering, 82(1):35–45, 1960. Publisher: American Society of Mechanical Engineers.
Lens Shiang, E. P., Chien, W. C., Lai, C. F., and Chao, H. C. Gated recurrent unit network based cellular trafile prediction. International
Conference on Information Networking, 2020-January:471–476, jan 2020.
Lorenz, E. N. Predictability: A problem partly solved. In Proc. Seminar on predictability, volume 1. Reading, 1996.
Lorenz, E. N. Predictability – a problem partly solved. In Palmer, T. and Hagedorn, R., editors, Predictability of Weather and Climate, pages 40–58. Cambridge University Press, 2006.
Mardani, M. R. and Ghanbari, M. Robust resource allocation scheme under channel uncertainties for lte-a systems. Wireless Networks, 25(3):1–13, may 2018.
Marsch, P., Bulakci, Ö., Queseth, O., and Boldi, M. 5G system design: architectural and functional considerations and long term research.
John Wiley & Sons, West Sussex, UK, 2018.
Morocho-Cayamcela, M. E., Lee, H., and Lim, W. Machine learning for 5g/b5g mobile and wireless communications: Potential, limitations, and future directions. IEEE Access, 7:137184–137206, 2019.
Ookla. Ookla 5G map, november 2022. Acessed on 21-Nov-2022.
Takens, F. Detecting strange attractors in turbulence. In Dynamical systems and turbulence, pages 366–381. Springer, Warwick, Coventry,
UK, 1981.
Teixeira, M. J. Alocação preditiva de recursos em sistemas de comunicações móveis. Ph.D. Thesis, Universidade Estadual de Campinas (Unicamp), 2021.
Teixeira, M. J. and Timoteo, V. S. Using a Kalman Filter to improve schedulers performance in mobile networks. In 2019 15th International
Wireless Communications and Mobile Computing Conference (IWCMC), pages 853–858, Tangier, Morocco, jun 2019. IEEE.
Teixeira, M. J. and Timóteo, V. S. A predictive resource allocation for wireless communications systems. SN Computer Science, 2(6):473, Sep 2021.
Teixeira, M. J. and Timóteo, V. S. Model-Free Predictor of Signal-to-Noise Ratios for Mobile Communications Systems. SN Computer Science, 4(4):345, Apr. 2023.
Trinh, H. D., Giupponi, L., and Dini, P. Mobile traffic prediction from raw data using lstm networks. IEEE International Symposium on Per-
sonal, Indoor and Mobile Radio Communications, PIMRC, 2018-September:1827–1832, dec 2018.
Wang, J.-B., Wang, J., Wu, Y., Wang, J.-Y., Zhu, H., Lin, M., and Wang, J. A machine learning framework for resource allocation assisted by
cloud computing. IEEE Network, 32(2):144–151, 2018.
Zaaraoui, H., Altman, Z., Altman, E., and Jimenez, T. Forecast scheduling for mobile users. IEEE International Symposium on Personal, In-
door and Mobile Radio Communications, PIMRC, dec 2016.
Zaidi, A., Athley, F., Medbo, J., Chen, X., and Durisi, G. 5G physical layer: Principles, models and technology components. Academic Press,
London, 2018.
Zhang, S. and Zhu, D. Towards artificial intelligence enabled 6g: State of the art, challenges, and opportunities. Computer Networks, 183:107556, dec 2020.