MIMO beam selection in 5G using neural networks

Authors

  • Julius Ruseckas Baltic Institute of Advanced Technology, Vilnius, Lithuania
  • Gediminas Molis Baltic Institute of Advanced Technology, Vilnius, Lithuania
  • Hanna Bogucka Poznan University of Technology http://orcid.org/0000-0002-1709-4862

Abstract

In this paper, we consider the cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple-input-multiple-output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user equipment to reduce latency in beam/cell identification.
Due to high path-loss in mmWave systems, the beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, a narrow beam must be formed. Thus, the number of possible beam orientations and consequently time needed for the discovery increases significantly when a random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars, and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable
performance for practical applications. This finding can be useful for user devices with limited processing power.

References

J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, ”What will 5G be?”, IEEE Journal on selected areas in communications32 (6), 1065–1082 (2014).

J. Liu, J. Wan, D. Jia, B. Zeng, D. Li, C. Hsu, and H. Chen, ”High-efficiency urban traffic management in context-aware computing and 5gcommunication”, IEEE Communications Magazine55(1), 34–40 (2017).

X. Cheng, L. Fang, L. Yang, and S. Cui, ”Mobile big data: The fuel for data-driven wireless”, IEEE Internet of Things Journal4(5), 1489–1516(2017).

K. Zheng, Z. Yang, K. Zhang, P. Chatzimisios, K. Yang, and W. Xiang, ”Big data-driven optimization for mobile networks toward 5G”, IEEE Network, 30, 44–51 (2016).

C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, ”Machine learning paradigms for next-generation wireless networks”, IEEE Wireless Communications24, 98–105 (2017).

W. B. Abbas and M. Zorzi, ”Context information based initial cell search for millimeter wave 5G cellular networks”, 2016 European Conference on Networks and Communications (EuCNC), pp. 111–116, 2016

I. Filippini, V. Sciancalepore, F. Devoti, and A. Capone, ”Fast CellDiscovery in mm-Wave 5G Networks with Context Information”, IEEE Transactions on Mobile Computing17(7), 1538–1552 (2018).

E. Ali, M. Ismail, R. Nordin, and N. F. Abdulah, ”Beamformingtechniques for massive MIMO systems in 5G: overview, classification, and trends for future research”, Frontiers of Information Technology &Electronic Engineering18(6), 753–772 (2017).

A. Habbal, S. I. Goudar, and S. Hassan, ”A Context-aware Radio Access Technology selection mechanism in 5G mobile network for smart city applications”, Journal of Network and Computer Applications, 135, 97–107, ISSN 1084-8045, (2019).

C.-L. Hwang and K. Yoon, ”Multiple Attribute Decision Making, Methods and Applications A State-of-the-Art Survey”, ISBN 978-3-642-48318-9, Springer-Verlag, Berlin, (1981).

A. Klautau, P. Batista, N. Gonzalez-Prelcic, Y. Wang and R. W. Heath,”5G MIMO Data for Machine Learning: Application to Beam-Selectionusing Deep Learning” in 2018 Information Theory and ApplicationsWorkshop (ITA) (2018).

NR Physical Layer Procedures for Control, Standard 3GPP, TS 38.213V16.5.0, 2021.

I. Aykin and M. Krunz, ”Efficient beam sweeping algorithms and initial access protocols for millimeter-wave networks”, IEEE Trans. Wireless Commun., 19(4), 2504–2514 (2020).

S. Tomasin, C. Mazzucco, D. De Donno and F. Cappellaro, ”Beam-Sweeping Design Based on Nearest Users Position and Beam in 5GmmWave Networks”, IEEE Access 8, 124402–124413 (2020).

T. S. Rappaport, G. R. MacCartney, S. Sun, H. Yan, and S. Deng, ”Small-scale, local area, and transitional millimeter wave propagation for 5G communications”, IEEE Trans. Antennas Propag. 65(12), 64, 74–6490(2017).

J. Gante, G. Falciao, and L. Sousa, ”Data-aided fast beamforming selection for 5G”, in Proc. IEEE Int. Conf. Acoust., Speech SignalProcess. (ICASSP), pp. 1183–1187, Apr. 2018.

V. Va, J. Choi, T. Shimizu, G. Bansal, and R. W. Heath, Jr., ”Inversemultipath fingerprinting for millimeter wave V2I beam alignment”, IEEE Trans. Veh. Technol. 67(5), 4042–4058 (2018).

V. Va, T. Shimizu, G. Bansal, and R. W. Heath, ”Online learning for position-aided millimeter wave beam training”, IEEE Access7, 30507–30526 (2019).

A. Klautau, N. Gonz ́alez-Prelcic, and R. W. Heath, ”LIDAR data for deep learning-based mmWave beam-selection”, IEEE Wireless Commun.Lett., 8(3), 909–912 (2019).

A. Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu, and D. Tujkovic, ”Deep learning coordinated beamforming for highly-mobile millimeter wave systems”, IEEE Access6, 37328–37348 (2018).

C. Anton-Haro and X. Mestre, ”Learning and data-driven beam selection for mmWave communications: An angle of arrival-based approach”, IEEE Access 7, 20404–20415 (2019).

M. S. Sim, Y. Lim, S. H. Park, L. Dai and C. Chae, ”Deep Learning-Based mmWave Beam Selection for 5G NR/6G With Sub-6 GHz Channel Information: Algorithms and Prototype Validation”, IEEE Access 8,51634–51646 (2020).

https://www.lasse.ufpa.br/raymobtime/

K. He, X. Zhang, S. Ren, J. Sun, ”Deep residual learning for image recognition” In: CVPR. (2016).

S. De and S. Smith, ”Batch normalization biases residual blocks towards the identity function in deep networks”, Advances in Neural Information Processing Systems 33 (2020).

L. N. Smith, ”A disciplined approach to neural network hyper-parameters: Part 1 – learning rate, batch size, momentum, and weight decay”, arXiv:1803.09820 [cs.LG] (2018).

A. Klautau, N. Gonzalez-Prelcic, and R. W. Heath, ”LIDAR Data for Deep Learning-Based mmWave Beam-Selection”, IEEE WirelessCommunications Letters, 8(3), 909–912 (2019).

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Published

2024-04-19

Issue

Section

Wireless and Mobile Communications