Authors

Yousif Mohamed Ali

Department of Computer Science, University of Khartoum, Sudan

Khalid Musa

Faculty of Computer Science and Information Technology, Al Neelain University, Sudan

Abdelrahim Ahmed

Faculty of Computer Science and Information Technology, Al Neelain University, Sudan

Hanan Yousif

Faculty of Computer Science and Information Technology, Al Neelain University, Sudan

Eman Siddig

Department of Computer Science, University of Khartoum, Sudan

Rasha Ahmed

Faculty of Information Technology, National Ribat University, Sudan

Abstract

The exponential growth of mobile data traffic and the densification of urban wireless networks have intensified the challenges of coverage limitations, signal attenuation, and energy inefficiency in fifth-generation (5G) communication systems. Massive Multiple-Input Multiple-Output (MIMO) technology enhances spectral efficiency and enables precise beamforming but suffers from high power consumption and hardware complexity. Reconfigurable Intelligent Surfaces (RIS), an emerging passive metasurface technology, provide energy-efficient signal reflection and propagation control without requiring active RF chains. This research proposes a hybrid architecture integrating a 64×64 massive MIMO array with strategically deployed RIS panels to optimize coverage and reduce power consumption. The study evaluates standalone MIMO, standalone RIS, and hybrid MIMO-RIS configurations through simulation-based analysis. Results demonstrate that while 64×64 MIMO ensures strong coverage at high energy cost, RIS reduces power consumption with limited standalone coverage. The hybrid system achieves nearly 100% improvement in coverage area and up to 60% reduction in power consumption compared to conventional massive MIMO systems. The findings highlight the potential of RIS-assisted MIMO systems as a practical and energy-efficient solution for next-generation 5G and beyond wireless networks.

Keywords

5G Wireless Communication; Massive MIMO (64×64); Reconfigurable Intelligent Surface (RIS); Beamforming; Energy Efficiency; Coverage Enhancement; Millimeter Wave (mmWave); Spectral Efficiency; Smart Radio Environment; Path Loss Reduction; Signal-to-Interference-plus-Noise Ratio (SINR); Hybrid MIMO–RIS Architecture; Power Optimization; Urban Propagation Modeling; Next-Generation Wireless Networks (B5G/6G)

Citation of this Article

Yousif Mohamed Ali, Khalid Musa, Abdelrahim Ahmed, Hanan Yousif, Eman Siddig, & Rasha Ahmed. (2026). Coverage Enhancement and Energy Optimization in 5G Networks Using Hybrid 64×64 Massive MIMO and Reconfigurable Intelligent Surfaces. Current Journal of Engineering and Science Research. 3(1), 23-28. Article DOI: https://doi.org/10.47001/CJESR/2026.301004

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. E. Björnson, J. Hoydis, and L. Sanguinetti, “Massive MIMO networks: Spectral, energy, and hardware efficiency,” Foundations and Trends in Signal Processing, vol. 11, no. 3–4, pp. 154–655, 2017.
  2. Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394–5409, 2019.
  3. M. Di Renzo et al., “Smart radio environments empowered by reconfigurable intelligent surfaces,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 11, pp. 2450–2525, 2020.
  4. S. Zhang and R. Zhang, “Capacity characterization for intelligent reflecting surface aided MIMO communication,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 8, pp. 1823–1838, 2020.
  5. 3GPP TR 38.901, “Study on channel model for frequencies from 0.5 to 100 GHz,” 3rd Generation Partnership Project, 2020.
  6. E. Basar et al., “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116753–116773, 2019.
  7. C. Huang et al., “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157–4170, 2019.
  8. J. Jeong, J. H. Oh, S. Y. Lee, Y. Park, and S.-H. Wi, “An Improved Path-Loss Model for Reconfigurable-Intelligent-Surface-Aided Wireless Communications and Experimental Validation,” IEEE Access, vol. 10, pp. 98065-98080, Sep. 2022, DOI: 10.1109/ACCESS.2022.3205117.
  9. H. Wang, Y. Zhou, and W. Sha, “Research on Wireless Coverage Area Detection Technology for 5G Mobile Communication Networks,” International Journal of Distributed Sensor Networks, vol. 13, no. 12, pp. 1–11, 2017, DOI: 10.1177/1550147717746352.
  10. P. Rahmawati, M. I. Nashiruddin, and M. A. Nugraha, “Capacity and Coverage Analysis of 5G NR Mobile Network Deployment for Indonesia’s Urban Market,” IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 2021, DOI: 10.1109/IAICT.2021.31.
  11. M. A. Affandi, M. A. Riyadi, and T. Prakoso, “Throughput and Coverage Evaluation on The Use of Existing Cellular Towers for 5G Network in Surakarta City,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 1, pp. 54-72, Mar. 2024, DOI: 10.26555/jiteki.v10i1.27719.
  12. K. Chen-Hu, G. C. Alexandropoulos, and A. G. Armada, “Non-Coherent Modulation for RIS-Empowered Multi-Antenna OFDM Communications,” arXiv preprint arXiv:2103.05547, Mar. 2021. [Online]. Available: arxiv.org.
  13. G. C. Alexandropoulos et al., “Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications,” arXiv preprint.