Open Access
Original Article

AI-Driven Spectrum Intelligence and Energy Optimization Techniques for 6G Networks: A Comprehensive Review

Radius: Journal of Science and Technology

View PDF

Volume 2, Issue 2,  Article Number: 252002 (2025)

Cover Page - RadiusJST

Home >> Radius >> Volume 2, Issue 2

Dadpe Kuldip1,*  |  Patil Dhurendra1  |  Kadam Prashant1  |  Raut Sachin1

1Vilasrao Deshmukh Foundation Group of Institutions, Latur – 413531, Maharashtra (India)

*Corresponding Author: entciok@gmail.com

Received: 19 October 2025 | Revised: 05 November 2025

Accepted: 11 November 2025 | Published Online: 14 November 2025

DOI: https://doi.org/10.5281/zenodo.17611239

© 2025 The Authors, under a Creative Commons license, Published by Scholarly Publication

Abstract

As wireless technology moves toward sixth generation (6G) networks, future wireless communication systems must be intelligent, self-optimizing, and energy-efficient. To address increasing pressure on spectrum, systems will need to integrate AI (artificial intelligence) and ML (machine learning) technologies to facilitate dynamic spectrum access, efficient energy distributions, and max data throughput. This review examines key AI-driven techniques for spectrum intelligence and energy optimization in 6G networks. The applications of spectrum prediction employing deep learning, reinforcement learning for dynamic spectrum allocation of resources, and federated learning approaches that provide distributed decision-making capabilities in heterogeneous wireless environments are highlighted. The roles of intelligent multiple-input multiple-output (MIMO) antennae and reconfigurable surfaces as design tools for maximizing the spectral efficiency and energy efficiency of systems is also examined. Recent designs in MIMO operating in sub-6 GHz have been employed as reference points for novel systems. Comparative analyses show that the AI enabled spectrum optimization techniques vastly outperform conventional static allocation in spectral efficiency, latency and energy efficiency. Finally, the review concludes with a discussion on the outstanding research challenges corresponding to trustworthy AI frameworks, sustainable energy frameworks, spectrum sharing etc., which will lead us to towards fully autonomous and energy efficient 6G networks.

Keywords

6G networks, Spectrum intelligence, Artificial intelligence, Energy optimization, MIMO antenna, Machine learning

References

  1. Saad, W., Bennis, M., & Chen, M. (2019). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34, 134-142.

[View Article]      [Google Scholar]

  1. Tataria, H., Shafi, M., Molisch, A. F., Dohler, M., Sjöland, H., & Tufvesson, F. (2021). 6G wireless systems: Vision, requirements, challenges, insights, and opportunities. Proceedings of the IEEE, 109, 1166-1199.

[View Article]      [Google Scholar]

  1. Strinati, E. C., Barbarossa, S., Gonzalez-Jimenez, J. L., Ktenas, D., Cassiau, N., Maret, L., & Dehos, C. (2019). 6G: The next frontier: From holographic messaging to artificial intelligence using subterahertz and visible light communication. IEEE Vehicular Technology Magazine, 14, 42-50.

[View Article]      [Google Scholar]

  1. Niyato, D., Kim, D. I., Han, Z., & Maso, M. (2016). Wireless powered communication networks: architectures, protocol designs, and standardization [Guest Editorial]. IEEE Wireless Communications, 23, 8-9.

[View Article]      [Google Scholar]

  1. Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20, 2595-2621.

[View Article]      [Google Scholar]

  1. Huang, C., Hu, S., Alexandropoulos, G. C., Zappone, A., Yuen, C., Zhang, R., & Debbah, M. (2020). Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends. IEEE wireless communications, 27, 118-125.

[View Article]      [Google Scholar]

  1. Maheswar, R., Kathirvelu, M., & Mohanasundaram, K. (2024). Energy Efficiency in Wireless Networks. Energies, 17, 417.

[View Article]      [Google Scholar]

  1. Trabelsi, N., Maaloul, R., Fourati, L. C., & Jaafar, W. (2024). Deep reinforcement learning for sleep control in 5G and beyond radio access networks: An overview. 2024 International Wireless Communications and Mobile Computing (IWCMC), IEEE, 1404-1411.

[View Article]      [Google Scholar]

  1. Al-Quraan, M., Mohjazi, L., Bariah, L., Centeno, A., Zoha, A., Arshad, K., & Imran, M. A. (2023). Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence, 7, 957-979.

[View Article]      [Google Scholar]

  1. Sharma, S., Mishra, A. K., Kumar, M. H., Deka, K., & Bhatia, V. (2024). Intelligent reflecting surfaces (IRS)-enhanced cooperative NOMA: A contemporary review. IEEE Access, 12, 82168-82191.

[View Article]      [Google Scholar]

  1. Lu, D. L. Y., Maman, L., Earls, J., Boag, A., & Baldi, P. (2025). Optimization of Antenna Array Configurations Using Deep Learning. IEEE Open Journal of Antennas and Propagation, 6, 1367-1374.

[View Article]      [Google Scholar]

  1. Sandeep, K., Sharma, N., & Narawade, N. (2025). High-isolation dual-band slotted patch MIMO antenna for sub-6 GHz 5G applications. International Journal of Advanced Technology and Engineering Exploration, 12, 301.

[View Article]      [Google Scholar]

  1. Sandeep K, Asmita D, Shrishail P, Shivale N, & Sonawane V. (2025). A novel four-element button mushroom MIMO antenna for enhanced sub-6 GHz 5G communication. Int Res J Multidiscip Scope (IRJMS), 6, 397–409.

[View Article]      [Google Scholar]

  1. Sandeep, K., Sharma, N., & Narwade, N. (2024). Sub-6 5G Networks: Design of Button Mushroom MIMO Antenna and it’s Investigation. International Journal of Innovations & Research Analysis, 4, 21-30.

[View Article]      [Google Scholar]

  1. Sandeep K, Sharma N, & Narawade N. (2024). Design of slotted patch MIMO antenna and investigation of antenna parameters for sub-6 5G network. Int Res J Multidiscip Scope, 5, 514-523.

[View Article]      [Google Scholar]

  1. Elsayed, M., & Erol-Kantarci, M. (2019). AI-enabled future wireless networks: Challenges, opportunities, and open issues. IEEE Vehicular Technology Magazine, 14, 70-77.

[View Article]      [Google Scholar]

  1. Bhattacharya, P., Patel, F., Alabdulatif, A., Gupta, R., Tanwar, S., Kumar, N., & Sharma, R. (2022). A deep-Q learning scheme for secure spectrum allocation and resource management in 6G environment. IEEE Transactions on Network and Service Management, 19, 4989-5005.

[View Article]      [Google Scholar]

  1. Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020). Federated learning for 6G communications: Challenges, methods, and future directions. China Communications, 17, 105-118.

[View Article]      [Google Scholar]

  1. Sur, S. N. & Bera, R. (2021). Intelligent reflecting surface assisted MIMO communication system: A review. Physical Communication, 47, 101386.

[View Article]      [Google Scholar]

  1. Das, S. K., Champagne, B., Psaromiligkos, I., & Cai, Y. (2024). A survey on federated learning for reconfigurable intelligent metasurfaces-aided wireless networks. IEEE Open Journal of the Communications Society, 5, 1846-1879.

[View Article]      [Google Scholar]

  1. Dieye, M., Jaafar, W., Elbiaze, H., & Glitho, R. H. (2023). DRL-based green resource provisioning for 5G and beyond networks. IEEE Transactions on Green Communications and Networking, 7, 2163-2180.

[View Article]      [Google Scholar]

  1. Khan, N., Coleri, S., Abdallah, A., Celik, A., & Eltawil, A. M. (2024). Explainable and robust artificial intelligence for trustworthy resource management in 6G networks. IEEE Communications Magazine, 62, 50-56.

[View Article]      [Google Scholar]

  1. Alabi, C. A., Imoize, A. L., Giwa, M. A., Faruk, N., & Tersoo, S. T. (2023, November). Artificial intelligence in spectrum management: policy and regulatory considerations. In 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). IEEE. 1-6.

[View Article]      [Google Scholar]

Cite This Article

D. Kuldip, P. Dhurendra, K. Prashant, and R. Sachin, “AI-Driven Spectrum Intelligence and Energy Optimization Techniques for 6G Networks: A Comprehensive Review,” Radius: Journal of Science and Technology 2(2) (2025) 252002. https://doi.org/10.5281/zenodo.17611239

Rights & Permission

This is an open access article published under the Creative Commons Attribution (CC BY) International License, which allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. No permission is needed to reuse this content under the terms of the license.
For uses not covered above, please contact the Scholarly Publication Rights Department.