Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | 02 | Page :
    By

    Bibhu Prasad Ganthia,

  1. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India

Abstract

The rapid expansion of digital communication infrastructure, driven by cloud computing, Internet of Things (IoT), 6G networks, and artificial intelligence applications, has significantly increased the energy consumption and carbon footprint of modern communication systems. Conventional optical communication networks often rely on static resource allocation and energy-intensive signal processing mechanisms, resulting in inefficient utilization of network resources and elevated operational costs. This study proposes a Green Artificial Intelligence (Green AI)-Enabled Opto-Electronic Communication System designed to support carbon- neutral digital networks through intelligent energy management, adaptive optical transmission, and sustainable network operation. The proposed framework integrates AI-driven traffic prediction, dynamic wavelength allocation, power-aware routing, and intelligent opto-electronic transceiver control to optimize energy consumption while maintaining high communication quality and reliability. Machine learning algorithms continuously monitor network conditions and user traffic demands, enabling real-time adjustment of optical resources and reduction of unnecessary power expenditure. Simulation results demonstrate that the proposed Green AI-enabled architecture achieves significant reductions in network energy consumption, transmission losses, and carbon emissions while improving spectral efficiency, throughput, and quality of service. Furthermore, the framework supports renewable-energy-integrated network infrastructures by adapting communication parameters according to energy availability and operational requirements. Comparative analysis with conventional optical communication systems reveals notable improvements in energy efficiency and sustainability metrics. The findings indicate that the integration of Green AI techniques with advanced opto- electronic communication technologies offers a promising pathway toward environmentally sustainable, intelligent, and carbon-neutral digital communication ecosystems. The proposed approach contributes to the development of next-generation green communication networks capable of meeting growing data demands while minimizing environmental impact and supporting global carbon reduction objectives.

Keywords: Green AI; Opto-Electronic Communication; Carbon-Neutral Networks; Energy Efficiency; Optical Networks.

How to cite this article:
Bibhu Prasad Ganthia. Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks. Trends in Opto-electro & Optical Communication. 2026; 16(02):-.
How to cite this URL:
Bibhu Prasad Ganthia. Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks. Trends in Opto-electro & Optical Communication. 2026; 16(02):-. Available from: https://journals.stmjournals.com/toeoc/article=2026/view=247307


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Ahead of Print Subscription Review Article
Volume 16
02
Received 18/06/2026
Accepted 20/06/2026
Published 23/06/2026
Publication Time 5 Days
1

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