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Review Article |

Optimizing Beamforming in Massive MIMO Systems Using Machine Learning Approaches: A Comprehensive Review

Author(s): Suhas Kakde1*,Dr. Sanjay Pokle2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 3

Publisher : FOREX Publication

Published : 30 September 2025

e-ISSN : 2347-470X

Page(s) : 515-523

DOI: https://doi.org/10.37391/IJEER.130316

Abstract

The development of the Massive Multiple-Input Multiple Output (MIMO) system in recent years has revolutionized wireless communication, delivering significant benefits to energy and spectrum efficiency. While these strategies have been instrumental in the continuous evolution of system performance, traditional static beamforming methods (e.g. Zero-Forcing (ZF), Maximum Ratio Transmission (MRT) and Minimum Mean Square Error (MMSE)) are limited concerning their scalability and reliability on channel state information to a large extent. In this paper, we investigate the techniques of beamforming with machine learning (ML) integration to bypass these limitations and better optimize system performance. This review covers the usage of different approaches to Machine Learning: supervised learning methods (including Neural Networks and Support Vector Machines (SVM). We also study reinforcement learning techniques due to their dynamic optimization features, as well as deep-learning models like Recurrent and Convolutional Neural Networks (RNN/CNN), which are popular for treating big data or temporal dynamics. Our analysis shows several key findings: ML-based methods are effective in improving the performance of beamforming, including enhancing spectral efficiency and reducing energy consumption as well as their robustness with respect to channel state information (CSI) errors. Finally, we conclude by identifying how potential emerging trends such as federated learning and quantum computing can be positioned to overcome these challenges in the future direction of ML-optimized beamforming for massive MIMO systems.

Keywords: Beamforming, Convolutional Neural Networks (CNN), Deep Learning (DL), Machine Learning (ML), Massive MIMO, Reinforcement Learning.




Suhas Kakde, Research Scholar, Electronics Engineering, School of Electrical and Electronics Engineering, Ramdeobaba University, Nagpur, Maharashtra, India; Email: suhas.kakde@gmail.com

Dr. Sanjay Pokle, Professor, Electronics & Communication Engineering Department, School of Electrical and Electronics Engineering, Ramdeobaba University, Nagpur, Maharashtra, India; Email: poklesb@rknec.edu

    [1] Mamillapally, K. C., & Dasari, R. K. (2024). Deep Learning-Based Channel Estimation and Beamforming Architecture for Massive MIMO Systems. Journal of The Institution of Engineers (India): Series B, 1-12.
    [2] Ilyas, B. R., Sofiane, B. M., Ali Abderrazak, T., & Miloud, K. (2024). Enhancing 5G massive MIMO systems with EfficientNet‐B7‐powered deep learning‐driven beamforming. Transactions on Emerging Telecommunications Technologies, 35(9), e5034.
    [3] Paranthaman, R. N., Sonker, A., Varalakshmi, S., Madiajagan, M., Daya Sagar, K. V., & Malathi, M. (2024). Reinforcement learning-based model for the prevention of beam-forming vector attacks on massive MIMO system. Optical and Quantum Electronics, 56(1), 44.
    [4] Srinivas, C. V., & Borugadda, S. A deep learning‐based channel estimation and joint adaptive hybrid beamforming approach for mm‐wave massive MIMO with group‐of‐subarrays. International Journal of Communication Systems, e5994.
    [5] Wang, Y., Gao, Z., Chen, S., Hu, C., & Zheng, D. (2024). Deep Learning–Based Channel Extrapolation and Multiuser beamforming for RIS-aided Terahertz Massive MIMO Systems over Hybrid-Field Channels. Intelligent Computing, 3, 0065.
    [6] Hojatian, H., Mlika, Z., Nadal, J., Frigon, J. F., & Leduc-Primeau, F. (2024). Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming. IEEE Transactions on Machine Learning in Communications and Networking.
    [7] Liu, J., & Zhang, H. (2024). Multi-Branch Unsupervised Learning-Based Beamforming in mm-Wave Massive MIMO Systems With Inaccurate Information. IEEE Transactions on Green Communications and Networking.
    [8] Tarafder, P., & Choi, W. (2023). Deep reinforcement learning-based coordinated beamforming for mmWave massive MIMO vehicular networks. Sensors, 23(5), 2772.
    [9] Bendjillali, R. I., Bendelhoum, M. S., Tadjeddine, A. A., & Kamline, M. (2023). Deep learning-powered beamforming for 5G massive MIMO Systems. Journal of Telecommunications and Information Technology, (4), 38-45.
    [10] Zhang, J., Zheng, G., Zhang, Y., Krikidis, I., & Wong, K. K. (2023). Deep learning based predictive beamforming design. IEEE Transactions on Vehicular Technology, 72(6), 8122-8127.
    [11] H. Ye, G. Y. Li, and B. H. Juang, "Deep reinforcement learning-based resource allocation for cellular networks," IEEE Transactions in Wireless Communications, vol. 19, no. 1, pp. 6-17, Jan. 2020. doi: 10.1109/TWC.2019.2943809.
    [12] H. Huang, Y. Song, J. Peng, and X. Yang, "Deep learning-based CSI feedback approach for time-varying massive MIMO channels," IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 743-747, Oct. 2020. doi: 10.1109/LWC.2020.2975513.
    [13] D. Xu, H. Chen, H. He, and Y. Zhang, "Deep reinforcement learning for dynamic beamforming in massive MIMO systems," IEEE Wireless Communications Letters, vol. 9, no. 10, pp. 1740-1744, Oct. 2020. doi: 10.1109/LWC.2020.3005505.
    [14] Qi, C., Wang, Y., & Li, G. Y. (2020). Deep learning for beam training in millimeter wave massive MIMO systems. IEEE Transactions on Wireless Communications.
    [15] Ahmed, I., Shahid, M. K., Khammari, H., & Masud, M. (2021). Machine learning based beam selection with low complexity hybrid beamforming design for 5G massive MIMO systems. IEEE Transactions on Green Communications and Networking, 5(4), 2160-2173.
    [16] Sohal, R. S., Grewal, V., Kaur, J., & Singh, M. L. (2022). Deep learning based analog beamforming design for millimetre wave massive MIMO system. Wireless Personal Communications, 126(1), 701-717.
    [17] Taha, A., Alrabeiah, M., & Alkhateeb, A. (2019, December). Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems. In 2019 IEEE Global communications conference (GLOBECOM) (pp. 1-6). IEEE.
    [18] Upadhyay, S., Juluru, T. K., Deshmukh, P. V., Pawar, A. P., Mane, S. C., Singh, C., & Shrivastava, A. (2024). Machine learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks. Journal of Electrical Systems, 20(3s), 971-979.
    [19] Ahmed, I., Shahid, M. K., Khammari, H., & Masud, M. (2021). Machine learning based beam selection with low complexity hybrid beamforming design for 5G massive MIMO systems. IEEE Transactions on Green Communications and Networking, 5(4), 2160-2173.
    [20] Liu, C., & Helgert, H. J. (2020). An improved adaptive beamforming-based machine learning method for positioning in massive mimo systems. Int. J. Adv. Internet Technol, 6, 1-12.
    [21] Lavdas, S., Gkonis, P. K., Zinonos, Z., Trakadas, P., Sarakis, L., & Papadopoulos, K. (2022). A machine learning adaptive beamforming framework for 5G millimeter wave massive MIMO multicellular networks. IEEE Access, 10, 91597-91609.
    [22] Guo, J., Wen, C. K., & Jin, S. (2020). Deep learning-based CSI feedback for beamforming in single-and multi-cell massive MIMO systems. IEEE Journal on Selected Areas in Communications, 39(7), 1872-1884.
    [23] Huang, S., Ye, Y., & Xiao, M. (2020). Hybrid beamforming for millimeter wave multi-user MIMO systems using learning machine. IEEE Wireless Communications Letters, 9(11), 1914-1918.
    [24] Al Kassir, H., Zaharis, Z. D., Lazaridis, P. I., Kantartzis, N. V., Yioultsis, T. V., & Xenos, T. D. (2022). A review of the state of the art and future challenges of deep learning-based beamforming. IEEE Access, 10, 80869-80882.
    [25] E. Björnson, J. Hoydis, and M. Debbah, "Massive MIMO networks: Spectral, energy, and hardware efficiency," Foundations and Trends® in Signal Processing, vol. 11, no. 3-4, pp. 154-655, 2017. doi: 10.1561/2000000093.
    [26] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, "Scaling up MIMO: Opportunities and challenges with very large arrays," IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 40-60, 2013. doi: 10.1109/MSP.2011.2178495.
    [27] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, "An overview of massive MIMO: Benefits and challenges," IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 742-758, Oct. 2014. doi: 10.1109/JSTSP.2014.2317671.
    [28] Q. Mao, F. Hu, and Q. Hao, "Deep learning for intelligent wireless networks: A comprehensive survey," IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2595-2621, 2018. doi: 10.1109/COMST.2018.2846401.
    [29] J. Zhang, L. Dai, and Z. Wang, "Machine learning and deep learning frameworks for massive MIMO systems: A comprehensive survey," IEEE Access, vol. 7, pp. 22926-22941, 2019. doi: 10.1109/ACCESS.2019.2896977.
    [30] L. Huang, Y. Xu, and Y. Wang, "Deep learning for massive MIMO CSI feedback," IEEE Wireless Communications Letters, vol. 8, no. 5, pp. 1357-1360, Oct. 2019. doi: 10.1109/LWC.2019.2922409.
    [31] H. Ye, G. Y. Li, and B. H. Juang, "Deep reinforcement learning-based resource allocation for cellular networks," IEEE Transactions on Wireless Communications, vol. 19, no. 1, pp. 6-17, Jan. 2020. doi: 10.1109/TWC.2019.2943809.
    [32] H. Huang, Y. Song, J. Peng, and X. Yang, "Deep learning-based CSI feedback approach for time-varying massive MIMO channels," IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 743-747, Oct. 2020. doi: 10.1109/LWC.2020.2975513.
    [33] Z. Yang, W. Liu, Y. Wang, and H. Li, "Deep learning-based channel estimation for massive MIMO with mixed-resolution ADCs," IEEE Access, vol. 7, pp. 89035-89045, 2019. doi: 10.1109/ACCESS.2019.2926610.
    [34] H. Sun, L. Yin, Y. Song, and H. Zou, "Reinforcement learning-based dynamic beamforming for massive MIMO systems," IEEE Transactions on Wireless Communications, vol. 17, no. 12, pp. 8032-8042, Dec. 2018. doi: 10.1109/TWC.2018.2875856.
    [35] [11] D. Xu, H. Chen, H. He, and Y. Zhang, "Deep reinforcement learning for dynamic beamforming in massive MIMO systems," IEEE Wireless Communications Letters, vol. 9, no. 10, pp. 1740-1744, Oct. 2020. doi: 10.1109/LWC.2020.3005505.
    [36] Huang, H., Song, Y., Yang, J., Gui, G., & Adachi, F. (2019). Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Transactions on Vehicular Technology, 68(3), 3027-3032.
    [37] Qi, C., Wang, Y., & Li, G. Y. (2020). Deep learning for beam training in millimeter wave massive MIMO systems. IEEE Transactions on Wireless Communications.
    [38] Ahmed, I., Shahid, M. K., Khammari, H., & Masud, M. (2021). Machine learning based beam selection with low complexity hybrid beamforming design for 5G massive MIMO systems. IEEE Transactions on Green Communications and Networking, 5(4), 2160-2173.

Suhas Kakde, and Dr. Sanjay Pokle(2025), Optimizing Beamforming in Massive MIMO Systems Using Machine Learning Approaches: A Comprehensive Review. IJEER 13(3), 515-523. DOI: 10.37391/IJEER.130316.