Towards Green AI-Native Networks: Evaluation of Neural Circuit Policy for Estimating Energy Consumption of Base Stations
Selim Ickin, Shruti Bothe, Aman Raparia, Nitin Khanna, Erik Sanders·April 03, 2025
Summary
NCPs for base station energy consumption estimation aim to optimize radio hardware and AI-based network management, offering reduced computational overhead, energy demand, and robust generalization compared to traditional ML models. They provide a low-cost, scalable solution for green AI-native networks, crucial for sustainable computing in telecommunications. AI and ML algorithms enhance future networks, focusing on energy efficiency and reduced carbon footprint. Deploying efficient models in base stations reduces infrastructure's carbon footprint, addressing challenges in managing diverse, data-distribution-changing models. Sparsely structured neural networks, like NCPs, offer dynamic adaptation during inference, improving generalization and reducing retraining needs. Continuous time networks are suitable for irregularly sampled datasets, valuable for telecommunications. This research proposes small, sparse, energy-efficient neural architectures for the telecom domain, focusing on sustainability goals in AI-native network design.
Introduction
Background
Overview of base station energy consumption challenges
Importance of energy-efficient solutions in telecommunications
Objective
Aim of the research: developing NCPs for base station energy consumption estimation
Key benefits: reduced computational overhead, energy demand, and robust generalization
Method
Data Collection
Types of data used for energy consumption estimation
Data sources and collection methods
Data Preprocessing
Techniques for preparing data for NCP models
Handling data distribution changes and irregularities
Model Development
Designing NCPs for base station energy consumption
Integration of AI and ML algorithms for enhanced efficiency
Model Evaluation
Metrics for assessing model performance
Comparison with traditional ML models
Application
Deployment in Base Stations
Implementation strategies for NCPs in real-world scenarios
Scalability and cost-effectiveness of the solution
Case Studies
Examples of successful NCP deployment in telecommunications
Impact on energy consumption and carbon footprint reduction
Future Directions
Research Opportunities
Advancements in NCP architecture and optimization
Integration with emerging AI technologies
Challenges and Solutions
Addressing limitations in model adaptation and scalability
Strategies for continuous improvement and innovation
Conclusion
Summary of Key Findings
Recap of the research outcomes and contributions
Implications for Sustainable Computing
Role of NCPs in achieving sustainable AI-native networks
Potential for broader application in green computing initiatives
Basic info
papers
neural and evolutionary computing
signal processing
machine learning
artificial intelligence
Advanced features