Call For Paper October 2026
Research Article | Open Access | Download PDF
Volume 13 | Issue 10 | Year 2026 | Article Id. IJECE-V13I10P105 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I10P105

Predictive Beam Management with Spatio-Temporal Hyper Networks and Reinforcement Intelligence for Sustainable Massive MIMO Architectures

G. Kanagavalli, P. Dhinakar

Received Revised Accepted Published
02 Jul 2026 12 Sep 2026 24 Sep 2026 28 Oct 2026

Citation :

G. Kanagavalli, P. Dhinakar, "Predictive Beam Management with Spatio-Temporal Hyper Networks and Reinforcement Intelligence for Sustainable Massive MIMO Architectures," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 68-82, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P105

Abstract

Massive MIMO is the basis of current 5G and future 6G wireless networks, with antenna gain significant spectral efficiency and network capacity, but is unable to support dynamic environments due to computational complexity, power consumption and CSI overhead. The research presents a single adaptive and energy-efficient beam management system, including hybrid analogue-digital beamforming and advanced intelligence based on learning. This system extends Deep Reinforcement Learning to optimize beams in real-time, graph-based learning to optimize beams selectively by topology, neuro-symbolic policy optimization to predict and study control, and spatio-temporal forecasting using hypernetworks to CSI, which cuts feedback costs by a major factor. The proposed framework reduces redundant CSI updates by dynamically changing beam patterns based on user movement, interference, as well as the changes in traffic conditions, and provides strong signals quality and low-latency adaptation. The main innovation is the combination design of prediction beam adaptation and energy aware CSI management. The performance of simulations shows that spectral efficiency, power and computing scalability have been significantly improved, making the solution a viable and future-proof architecture of intelligent massive MIMO network.

Keywords

Beam management, Channel state information overhead, Energy efficiency, Mobility awareness, Network scalability, Spectral efficiency.

References

  1. Keunyoung Kim et al., “Beamforming and Power Optimization for User Fairness in Cell-Free MIMO Systems,” IEEE Access, vol. 11, pp. 51032-51046, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Maksym Girnyk, Henrik Jidhage, and Sebastian Faxér, “Broad Beamforming Technology in 5G Massive MIMO,” Ericsson Technology Review, vol. 2023, no. 10, pp. 2-6, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Ali Hosseinalipour Jazi, S. Mohammad Razavizadeh, and Tommy Svensson, “Integrated Access and Backhaul (IAB) in Cell-Free Massive MIMO Systems,” IEEE Access, vol. 11, pp. 71658-71667, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Boyu Ning et al., “Beamforming Technologies for Ultra-Massive MIMO in Terahertz Communications,” IEEE Open Journal of the Communications Society, vol. 4, pp. 614-658, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Panagiotis K. Gkonis, “A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges,” IEEE Access, vol. 11, pp. 67-88, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Hamed Hojatian et al., “Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 939-955, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Marwa Abdelfatah, Abdelhalim Zekry, and Shaimaa ElSayed, “User Selection Methods for Overcoming Growing Number of Served Users in Massive MIMO Systems,” IEEE Access, vol. 11, pp. 59981-59994, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Israa Khaled et al., “Angle-Domain Hybrid Beamforming-Based mmWave Massive MIMO-NOMA Systems,” IEEE Open Journal of the Communications Society, vol. 4, pp. 684-699, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Kengo Ando, Koji Ishibashi, and Giuseppe Thadeu Freitas de Abreu, “Robust Tensor Decomposition for Heterogeneous Beamforming under Imperfect Channel State Information,” IEEE Open Journal of Signal Processing, vol. 4, pp. 89-98, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. Rafid Umayer Murshed et al., “A CNN-LSTM-Based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming,” IEEE Access, vol. 11, pp. 38614-38630, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Mobeen Mahmood et al., “Achieving Capacity Gains in Practical Full-Duplex Massive MIMO Systems: A Multi-Objective Optimization Approach Using Hybrid Beamforming,” IEEE Open Journal of the Communications Society, vol. 5, pp. 2268-2286, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Paweł Bieńkowski et al., “Simplified Methodology of Electromagnetic Field Measurements in the Vicinity of 5G Massive MIMO Base Station for Environmental Exposure Assessment,” IEEE Access, vol. 12, pp. 8071-8080, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Wilson De Souza Junior et al., “Manifold-Based Optimizations for RIS-Aided Massive MIMO Systems,” IEEE Open Journal of the Communications Society, vol. 5, pp. 7913-7940, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Spandan Bisoyi et al., “Outdoor Massive MIMO Testbed with Directional Beams: Design, Implementation, and Validation,” IEEE Access, vol. 13, pp. 22230-22242, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Mohsen Hossienzadeh, Hassan Aghaeinia, and Mohammad Kazemi, “Deep Learning Based Interference Exploitation in 1-Bit Massive MIMO Precoding,” IEEE Access, vol. 11, pp. 17096-17103, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Arselan Ashraf et al., “Advancements and Challenges in Scalable Modular Antenna Arrays for 5G Massive MIMO Networks,” IEEE Access, vol. 12, pp. 57895-57916, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Iqra Farhat, Umar Rashid, and Omer Waqar, “Enhanced Fronthaul Capacity in CRANs: Sum-Rate Maximization via Joint Optimal Design of STAR-RIS, Massive MIMO and Data Compression,” IEEE Open Journal of Vehicular Technology, vol. 6, pp. 202-215, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Joarder Jafor Sadique, Imtiaz Nasim, and Ahmed S. Ibrahim, “Covariance Shaping over Riemannian Manifolds for Massive MIMO Communication,” IEEE Access, vol. 11, pp. 142874-142883, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Nura Abdalrhman Alhaj et al., “Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation toward 6G Requirements: A Review,” IEEE Access, vol. 12, pp. 483-513, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Xuan-Toan Dang, Hieu V. Nguyen, and Oh-Soon Shin, “Optimization of IRS-NOMA-Assisted Cell-Free Massive MIMO Systems Using Deep Reinforcement Learning,” IEEE Access, vol. 11, pp. 94402-94414, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Aymen Ktari, and Ghaya Rekaya, “Neural Networks for Multi User Beam Management in mmWave Massive MIMO,” 2024 IEEE Middle East Conference on Communications and Networking (MECOM), Abu Dhabi, United Arab Emirates, pp. 94-99, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. Yunke Mo, Fangfang Fan, and Xinyi Zhao, “A Compact Wide-Angle Scanning Antenna Array for U6G Massive MIMO Base Station,” 2025 IEEE MTT-S International Wireless Symposium (IWS), Xi'an, China, pp. 1-3, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  23. Zhenhai Zhu, Yuan Wu, and Jiajia Hao, “Research on MIMO Beam Integration Optimization Method Based on PredRNN Prediction and Ant Colony Algorithm,” 2024 14th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Hefei, China, pp. 1-4, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  24. Yuhong Wang, and Jingyi Peng, “Beam Training for Spatial Non-Stationary XL-MIMO,” 2023 3rd International Conference on Intelligent Communications and Computing (ICC), Nanchang, China, pp. 228-231, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Margaux Pellet et al., “Multibeam Phased Arrays Exploiting Frequency Dispersion for Massive MIMO Satellite Communications,” 2024 18th European Conference on Antennas and Propagation (EuCAP), Glasgow, United Kingdom, pp. 1-5, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Zhirong Zhang, “Massive MIMO Technology: A Key Advancement in Shaping Communication Networks for the 5G/5G+ Era,” 2024 International Wireless Communications and Mobile Computing (IWCMC), Ayia Napa, Cyprus, pp. 274-278, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Yuki Sasaki, Kabuto Arai, and Kazuki Maruta, “QRD-based Precoding Design for Multiuser Massive MIMO Null-Space Expansion,” IEICE Communications Express, vol. 13, no. 12, pp. 509-512, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Yunwu Wang et al., “Energy-Efficient and QoS-Guaranteed 3-D Beam Mapping for Massive MIMO System Under Tidal Traffic Loads,” IEEE Internet of Things Journal, vol. 11, no. 21, pp. 35023-35035, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Amar D. Chaudhari, and Soumava Mukherjee, “Exploiting the Viability of Antenna Arrays for Multi-Band FR2 Base-Station Massive MIMO Systems,” 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India, pp. 1143-1147, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Wassim Ben Chikha et al., “Radio Environment Map based Inter-Cell Interference Coordination for Massive-MIMO Systems,” IEEE Transactions on Mobile Computing, vol. 23, no. 1, pp. 785-796, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]