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Paper ID:IJSREM63953

OPTIMIZATION OF FRAMEWORK FOR 6G WIRELESS NETWORKS USING AI BASED MACHINE LEARNING TECHNIQUES

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63953

Volume:

10 |

5 |

May 2026

Pages:

1-15

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RU

RICHA UMARIYA

Department of Electronics and Communication Engineering

Barkatullah university Institute of Technology

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PJ

Priyesh Jaiswal

Department of Electronics and Communication Engineering

Barkatullah university Institute of Technology

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Abstract

Sixth-generation (6G) wireless communication systems are expected to support extremely high data rates, massive connectivity, and intelligent services. However, the rapid growth of network devices and data traffic will significantly increase energy consumption in future communication networks. Therefore, improving energy efficiency has become a critical challenge for 6G systems. This research proposes an artificial intelligence-based framework for energy optimization in 6G wireless networks. Machine learning techniques are used to analyze network conditions and dynamically allocate resources to minimize power consumption while maintaining high communication performance. A simulation model is developed using MATLAB/Python to evaluate the performance of the proposed AI-based algorithm. Performance metrics such as energy efficiency, throughput, and latency are analysed and compared with conventional resource allocation techniques. Simulation results demonstrate that the proposed AI-based approach significantly reduces energy consumption while maintaining reliable communication. The proposed framework provides a promising solution for sustainable and energy-efficient 6G communication networks.

KEYWORDS

Keywords: 6G NETWORKS OPTIMIZATION IN 6G AI- BASED ALGORITHM

Cite This Article

RICHA UMARIYA, Priyesh Jaiswal, "OPTIMIZATION OF FRAMEWORK FOR 6G WIRELESS NETWORKS USING AI BASED MACHINE LEARNING TECHNIQUES," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-15, May 2026.

Full Article/Conclusion

·                  The proposed AI model dynamically adjusts transmission power and resource allocation. ·                  Energy consumption decreases when base stations operate in sleep mode during low traffic periods. ·                  Intelligent scheduling reduces unnecessary transmissions.

References

[1] International Telecommunication Union, Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond, Geneva, Switzerland, 2023. [2] T. S. Rappaport, Y. Xing, O. Kanhere, et al., “Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond,” IEEE Access, vol. 7, pp. 78729–78757, 2019. [3] W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems,” IEEE Network, vol. 34, no. 3, pp. 134–142, 2020. [4] M. Chen, U. Challita, W. Saad, et al., “Artificial Neural Networks-Based Machine Learning for Wireless Networks,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019. [5] S. Dang, O. Amin, B. Shihada, and M. Alouini, “What Should 6G Be?,” Nature Electronics, vol. 3, no. 1, pp. 20–29, 2020. [6] Z. Zhang, Y. Xiao, Z. Ma, et al., “6G Wireless Networks: Vision, Requirements, Architecture and Key Technologies,” IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 28–41, 2019. [7] Y. Sun, M. Peng, and S. Mao, “Deep Reinforcement Learning Based Mode Selection and Resource Management,” IEEE Transactions on Communications, vol. 67, no. 10, pp. 7095–7109, 2019. [8] C. Huang, A. Zappone, M. Debbah, et al., “Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication,” IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157–4170, 2019. [9] T. O'Shea and J. Hoydis, “An Introduction to Deep Learning for the Physical Layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563–575, 2017. [10] N. C. Luong et al., “Applications of Deep Reinforcement Learning in Communications and Networking,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3133–3174, 2019.

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Publication Date

5/25/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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