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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63953
Volume:
10 |
5 |
May 2026
Pages:
1-15
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Electronics and Communication Engineering
Barkatullah university Institute of Technology
Department of Electronics and Communication Engineering
Barkatullah university Institute of Technology
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
· 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.
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Publication Date
5/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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