Warianty tytułu
Adaptive beamforming in mm-wave 5G networks
Konferencja
Konferencja Radiokomunikacji i Teleinformatyki (20-22.09.2023 ; Kraków, Polska)
Języki publikacji
Abstrakty
W artykule przedstawiono zastosowanie podejścia opartego o uczenie maszynowe dla rozwiązania problemu kształtowania charakterystyki promieniowania (ang. beamforming, BF) w milimetrowych sieciach 5G. W procesie modelowania BF wykorzystano problem wielorękich bandytów, w tym algorytm Exp3. Dzięki jego użyciu znaleziono przybliżone rozwiązanie tego problemu. Wyniki symulacji potwierdziły, że proponowany schemat jest porównywalny pod względem efektywności energetycznej ze znanymi rozwiązaniami.
The article presents the use of a machine learning approach for the solution beamforming, BF) in millimeter 5G networks. The multi-armed bandit problem, including the Exp3 algorithm, was used in the BF modeling process. Thanks to its use found an approximate solution to this problem. The simulation results confirmed that the proposed scheme is comparable in terms of energy efficiency with known solutions.
Rocznik
Tom
Strony
482--486
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
- Wydział Matematyki i Informatyki, UJ, Kraków, jerzy.martyna@uj.edu.pl
Bibliografia
- [1] Rangan S., Rappaport T.S., Erkip E. 2014. Millimeter wave cellular wireless networks: potentials and challenges, Proc. of the IEEE, 102(3) : 366 - 385.
- [2] Roh W., Seol J.Y., Park J., Lee B., Lee J., Kim Y., Cho J., Cheun K., Aryanfar F. 2014. Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results, IEEE Communication Magazine, 52(2) : 106 - 113.
- [3] Samsung, Massive MIMO for New Radio, 2020. [Online]. Available: https://www.samsung.com/global/business/networks/ insights/white-papers/1208-massive-mimo-for-newradio/
- [4] Liu Y., Qin Z., Elkashlan M., Ding Z., Nallanathan A., Hanzo L. 2017. Nonorthogonal Multiple Access for 5G and Beyond, Proceedings of the IEEE, 105(12): 2347 - 2381.
- [5] Gotsis K.A., Sahalos J.N. 2011. Beamforming in 3G and 4G mobile communications: the switched-beam approach. In: Maícas, J.P. (Ed.), A Multidisciplinary Approach. InTech, 201-216.
- [6] Hur, S., Kim, T., Love, D.J., Krogmeier D.J., Thomas J.V., Ghosh T.A. 2013. Millimeter wave beamforming for wireless backhaul and access in small cell networks. IEEE Transactions on Communications, 61(10) : 4391 - 4403.
- [7] Bogale T.E., Le L.B., Haghighat A., Vandendorpe L. 2016. On the number of RF chains and phase shifters, and scheduling design with hybrid analog–digital beamforming. IEEE Transactions on Wireless Communications, 15(5) : 3311 - 3326.
- [8] Zhang J., Yu X., Letaief K.B. 2020. Hybrid Beamforming for 5G and Beyond Millimeter-Wave Systems: A Holistic View, IEEE Open Journal of the Communications Society, 1: 77-91.
- [9] Satyanarayana K., El-Hajjar M., Mourad A.A., Hanzo L. 2019. Multi-user hybrid beamforming relying on learning-aided link-adaptation for mmWave systems. IEEE Access, 7: 23197-23209.
- [10] Chen, J., Feng, W., Xing, J., Yang, P., Sobelman, G. E., Lin, D., Li, S. (2020). Hybrid beamforming/combining for millimeter wave MIMO: A machine learning approach. IEEE Transactions on Vehicular Technology, 69(10) : 11353-11368.
- [11] Hojatian H., Nadal J., Frigon J. F., Leduc-Primeau F. 2021. Unsupervised deep learning for massive MIMO hybrid beamforming. IEEE Transactions on Wireless Communications, 20(11): 7086 - 7099.
- [12] Long Y., Chen Z., Fang J., Tellambura C. 2018. Datadriven-based analog beam selection for hybrid beamforming under mm-wave channels. IEEE Journal of Selected Topics in Signal Processing, 12(2) : 340-352.
- [13] Gatzianas, M., Kalfas, G., Vagionas, C., Mesodiakaki, A. 2019. Downlink coordinated beamforming policies for 5G millimeter wave dense networks. In 2019 European Conference on Networks and Communications (EuCNC) : 342 - 346.
- [14] Widrow B., Manty P.E., Griffiths L.J., Goode B.B. 1967. Adaptive Antenna Systems, Proc. IEEE, 55(12) : 2143 - 2159.
- [15] Monzingo R., Miller T. 1980. Introduction to Adaptive Arrays, Wiley Interscience„ John Wiley and Sons, New York.
- [16] Auer P., Cesa-Bianchi N., Fischer P. 2002. Finite-time Analysis of the Multiarmed Bandit Problem, Machine Learning, (47) : 235 - 256.
- [17] Seldin Y., Szepesvári C., Auer P., Abbasi-Yadkori Y. 2012. Evaluation and Analysis of the Performance of the EXP3 Algorithm in Stochastic Environments. In 10th European Workshop on Reinforcement Learning, 103 - 116.
- [18] Bogale T.E., Le L.B. 2014. Beamforming for multiuser massive MIMO systems: Digital versus hybrid analog-digital. In 2014 IEEE Global Communications Conference (GLOBECOM): 4066-4071.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-be5f2f06-da92-4475-b286-27c928e45176