Spectral Efficiency of mmWave Massive MIMO Systems with Hybrid Beamforming
DOI:
https://doi.org/10.31185/wjes.Vol13.Iss4.700Keywords:
Massive multiple-input multiple-output, Bandwidth Efficiency, Millimeter Waves, fully-connected Structure, hybrid beamforming, manifold geometricAbstract
Millimeter wave communications (mmWave) has been considered a key enabling technique for 5G systems since it offers orders of magnitude more spectrum than current frequency bands. Unlike conventional multiple input multiple output (MIMO) networks, beamforming in millimeter-wave systems cannot be accomplished using digital techniques since only a limited number of analog-to-digital converters and mixers can be handled due to their cost and power consumption. Recently, there has been a lot of interest in a hybrid beamforming transceiver design, which includes an analog and digital beamformer, as a less expensive alternative. However, the optimal design for these hybrid beamformers has not yet been fully established. In order to approach the complex performance of the fully digital beamformer, this work will provide efficient alternative minimization techniques for a hybrid beamforming structure, namely the manifold-based geometric fully-connected antennas. According to simulation data on bandwidth efficiency, the proposed iterative algorithm performs much better than the baseline hybrid beamforming approach, namely the MMSE-MP algorithm (about 6.8% for a particular situation). Additionally, based on the recommended techniques, comparing the simulation results of the two hybrid beamforming structures will yield vital design information.
References
[1] J. G. Andrews et al., “What will 5G be?,” IEEE J. Sel. areas Commun., vol. 32, no. 6, pp. 1065–1082, 2014. DOI: https://doi.org/10.1109/JSAC.2014.2328098
[2] H. Fahad and I. Hburi, “ConvNet-based multi-antenna precoder design for green communication,” in Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 4, Springer, 2021, pp. 357–365. DOI: https://doi.org/10.1007/978-981-16-2102-4_33
[3] J. Hoydis, S. Ten Brink, and M. Debbah, “Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?,” IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 160–171, 2013. DOI: https://doi.org/10.1109/JSAC.2013.130205
[4] C. Li, J. Zhang, and K. B. Letaief, “Throughput and energy efficiency analysis of small cell networks with multi-antenna base stations,” IEEE Trans. Wirel. Commun., vol. 13, no. 5, pp. 2505–2517, 2014. DOI: https://doi.org/10.1109/TWC.2014.031714.131020
[5] I. Hburi and H. F. Khazaal, “A Neural Network Approach for Spectral and Energy Efficient Multiple Antenna Systems,” in 2021 1st Babylon International Conference on Information Technology and Science (BICITS), IEEE, 2021, pp. 154–159. DOI: https://doi.org/10.1109/BICITS51482.2021.9509906
[6] G. Bartoli et al., “Beamforming for small cell deployment in LTE-advanced and beyond,” IEEE Wirel. Commun., vol. 21, no. 2, pp. 50–56, 2014. DOI: https://doi.org/10.1109/MWC.2014.6812291
[7] N. Qasim, I. Hburi, and H. S. Al Ammar, “Intelligent Reconfigurable surface technique for Multiple Antenna Communication System,” Wasit J. Eng. Sci., vol. 12, no. 3, pp. 15–24, 2024. DOI: https://doi.org/10.31185/ejuow.Vol12.Iss3.507
[8] Y. Shi, J. Zhang, K. B. Letaief, B. Bai, and W. Chen, “Large-scale convex optimization for ultra-dense cloud-RAN,” IEEE Wirel. Commun., vol. 22, no. 3, pp. 84–91, 2015. DOI: https://doi.org/10.1109/MWC.2015.7143330
[9] Y. Shi, J. Zhang, and K. B. Letaief, “Group sparse beamforming for green cloud-RAN,” IEEE Trans. Wirel. Commun., vol. 13, no. 5, pp. 2809–2823, 2014. DOI: https://doi.org/10.1109/TWC.2014.040214.131770
[10] I. Hburi, A. Assad, and H. Fahad, “A Deep Neural Network Approach to Max-Min Fair Precoder for Multiple Antenna Systems,” in 2022 3rd Information Technology To Enhance e-learning and Other Application (IT-ELA), IEEE, 2022, pp. 123–128. DOI: https://doi.org/10.1109/IT-ELA57378.2022.10107962
[11] S. Hur, T. Kim, D. J. Love, J. V Krogmeier, T. A. Thomas, and A. Ghosh, “Millimeter wave beamforming for wireless backhaul and access in small cell networks,” IEEE Trans. Commun., vol. 61, no. 10, pp. 4391–4403, 2013. DOI: https://doi.org/10.1109/TCOMM.2013.090513.120848
[12] E. Torkildson, U. Madhow, and M. Rodwell, “Indoor millimeter wave MIMO: Feasibility and performance,” IEEE Trans. Wirel. Commun., vol. 10, no. 12, pp. 4150–4160, 2011. DOI: https://doi.org/10.1109/TWC.2011.092911.101843
[13] T. S. Rappaport, R. W. Heath Jr, R. C. Daniels, and J. N. Murdock, Millimeter wave wireless communications. Pearson Education, 2015.
[14] M. R. Akdeniz et al., “Millimeter wave channel modeling and cellular capacity evaluation,” IEEE J. Sel. areas Commun., vol. 32, no. 6, pp. 1164–1179, 2014. DOI: https://doi.org/10.1109/JSAC.2014.2328154
[15] O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Trans. Wirel. Commun., vol. 13, no. 3, pp. 1499–1513, 2014. DOI: https://doi.org/10.1109/TWC.2014.011714.130846
[16] P. Jain, P. Netrapalli, and S. Sanghavi, “Low-rank matrix completion using alternating minimization,” in Proceedings of the forty-fifth annual ACM symposium on Theory of computing, 2013, pp. 665–674. DOI: https://doi.org/10.1145/2488608.2488693
[17] M. Cai, “Modeling and mitigating beam squint in millimeter wave wireless communication,” 2018, University Of Notre Dame.
[18] A. A. Majeed, D. Ali Saed, and I. Hburi, “AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems,” Int. J. Electr. Comput. Eng. Syst., vol. 14, no. 8, pp. 843–851, 2023. DOI: https://doi.org/10.32985/ijeces.14.8.3
[19] P. Drineas and M. W. Mahoney, “Approximating a gram matrix for improved kernel-based learning,” in International Conference on Computational Learning Theory, Springer, 2005, pp. 323–337. DOI: https://doi.org/10.1007/11503415_22
[20] W. Roh et al., “Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results,” IEEE Commun. Mag., vol. 52, no. 2, pp. 106–113, 2014. DOI: https://doi.org/10.1109/MCOM.2014.6736750
[21] S. Sun, T. S. Rappaport, R. W. Heath, A. Nix, and S. Rangan, “MIMO for millimeter-wave wireless communications: Beamforming, spatial multiplexing, or both?,” IEEE Commun. Mag., vol. 52, no. 12, pp. 110–121, 2014. DOI: https://doi.org/10.1109/MCOM.2014.6979962
[22] A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel estimation and hybrid precoding for millimeter wave cellular systems,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 5, pp. 831–846, 2014. DOI: https://doi.org/10.1109/JSTSP.2014.2334278
[23] P. Wang, Y. Li, L. Song, and B. Vucetic, “Multi-gigabit millimeter wave wireless communications for 5G: From fixed access to cellular networks,” IEEE Commun. Mag., vol. 53, no. 1, pp. 168–178, 2015. DOI: https://doi.org/10.1109/MCOM.2015.7010531
[24] S. Rangan, T. S. Rappaport, and E. Erkip, “Millimeter-wave cellular wireless networks: Potentials and challenges,” Proc. IEEE, vol. 102, no. 3, pp. 366–385, 2014. DOI: https://doi.org/10.1109/JPROC.2014.2299397
[25] C. Zhang et al., “Compact Millimeter Wave Massive MIMO System Utilizing ESPAR,” IEEE Trans. Commun., 2025. DOI: https://doi.org/10.1109/TCOMM.2025.3568223
[26] K. Chen, H. Ding, Y. Zhu, Z. Yang, and B. Li, “Hybrid beamforming optimization design for millimeter-wave communications: A multi-scale convolutional neural network approach,” AEU-International J. Electron. Commun., p. 155913, 2025. DOI: https://doi.org/10.1016/j.aeue.2025.155913
[27] Y. Chen, H. Shen, and C. Han, “Cross Far-and Near-Field Beam Management Technologies in Millimeter-Wave and Terahertz MIMO Systems,” arXiv Prepr. arXiv2504.18855, 2025. DOI: https://doi.org/10.1109/OJVT.2025.3631629
[28] K. Chen, C. Qi, J. Huang, O. A. Dobre, and G. Y. Li, “Near-Field Communications for Extremely Large-Scale MIMO: A Beamspace Perspective,” IEEE Commun. Mag., 2025. DOI: https://doi.org/10.1109/MCOM.001.2400182
[29] M. Majidzadeh, J. Kaleva, N. Tervo, H. Pennanen, A. Tölli, and M. Latva-aho, “Hybrid Beamforming for Mm-Wave massive MIMO systems with partially connected RF Architecture,” Wirel. Pers. Commun., vol. 136, no. 4, pp. 1947–1979, 2024. DOI: https://doi.org/10.1007/s11277-024-11026-1
[30] T. Sun, G. Zhu, X. Li, J. Fan, and M. Xia, “Low-complexity hybrid beamforming for multi-cell mmwave massive MIMO: A primitive Kronecker decomposition approach,” Signal Processing, p. 110102, 2025. DOI: https://doi.org/10.1016/j.sigpro.2025.110102
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sajjad Kadhim, Dr. Hasan Fahad Khazaal, Dr. Hamed Al-Raweshidy, Dr. Ismail Hburi, Ruwa Mohammed, Asmaa Aliwy

This work is licensed under a Creative Commons Attribution 4.0 International License.

