AI-Based Energy Optimization in Communication Systems

Modern communication systems, including 5G, IoT, and satellite networks, are driving a sharp rise in global energy consumption. As sustainability becomes a major concern, optimizing energy usage without compromising network performance is critical. Traditional methods struggle to manage the complexity and dynamic behavior of today’s networks.



Artificial Intelligence (AI) offers a promising solution by enabling intelligent, adaptive energy management. Techniques like machine learning and reinforcement learning can analyze real-time data to predict traffic, optimize routing, manage power levels, and enhance overall network efficiency. Applications span energy-aware scheduling, green IoT communications, adaptive base station control, and efficient edge computing.

The goal of this special issue is to explore and promote innovative research on the application of Artificial Intelligence (AI) for energy optimization in communication systems. It aims to bring together interdisciplinary contributions that demonstrate how AI techniques—such as machine learning, deep learning, and reinforcement learning—can enhance energy efficiency across various layers of modern communication networks, including 5G, IoT, satellite, and edge systems. By addressing challenges such as dynamic resource allocation, intelligent power control, and real-time traffic prediction, this issue seeks to support the development of sustainable, intelligent, and eco-friendly communication infrastructures. The special issue encourages submissions of novel methodologies, architectures, theoretical advancements, and real-world case studies that collectively contribute to greener and more efficient communication technologies.

This special issue focuses on the integration of Artificial Intelligence (AI) techniques to enhance energy efficiency in modern communication systems. As communication networks continue to evolve with the proliferation of 5G/6G, IoT, and edge computing, there is an urgent need for intelligent solutions that reduce energy consumption while maintaining high performance and quality of service.



The issue welcomes original research articles, review papers, and case studies that address theoretical, methodological, or practical aspects of AI-driven energy optimization.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Artificial Intelligence in Communication Systems, Energy-Efficient Wireless Communication, Machine Learning for Energy Optimization, Energy Harvesting in IoT Sustainable Communication Systems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.