AI2030 Responsible AI Tools Marketplace

Key Takeaways

AI transparency is critical for infrastructure decisions: The paper demonstrates that explainable AI (XAI) techniques can reveal which specific network parameters drive energy consumption in radio networks, making AI recommendations understandable to engineers and operators rather than treating them as "black boxes."
Open network standards enable better AI oversight: Open Radio Access Networks (Open RAN) allow data collection across all network layers, which not only improves AI model development but also creates opportunities to audit and understand how AI systems make energy efficiency recommendations.
Multiple factors influence network energy use: The research identifies several key parameters affecting energy consumption—including airtime, data throughput, buffer status, and resource allocation—suggesting that responsible energy optimization requires understanding complex interactions rather than relying on single metrics.
Explainability supports sustainable technology goals: By integrating XAI into energy-efficient network design, the work shows how transparency can help achieve environmental objectives, allowing stakeholders to verify that AI-driven energy savings are genuine and not simply shifting consumption elsewhere in the system.
Interpretability enables better governance of AI in critical infrastructure: Understanding which RAN parameters XAI identifies as important allows network operators, regulators, and engineers to validate whether AI recommendations align with broader network reliability and sustainability goals before deployment.

Abstract

The Open Radio Access Network (Open RAN) is an emerging idea -- transforming the traditional Radio Access Networks (RAN) that are monolithic and inflexible into more flexible and innovative. By leveraging open standard interfaces, data collection across all RAN layers becomes feasible, paving the way for the development of energy-efficient Open RAN architectures through Artificial Intelligence / Machine Learning (AI/ML). However, the inherent complexity and black-box nature of AI/ML models used for energy consumption prediction pose challenges in interpreting their underlying factors and relationships. This work presents an integration of eXplainable AI (XAI) to understand the key RAN parameters that contribute to energy consumption. Furthermore, the paper delves into the analysis of RAN parameters -- \emph{airtime}, \emph{goodput}, \emph{throughput}, \emph{buffer status report}, \emph{number of resource blocks}, and many others -- identified by XAI techniques, highlighting their significance in energy consumption.

Accepted at FNFW 2024

Key Takeaways

AI transparency is critical for infrastructure decisions: The paper demonstrates that explainable AI (XAI) techniques can reveal which specific network parameters drive energy consumption in radio networks, making AI recommendations understandable to engineers and operators rather than treating them as "black boxes."
Open network standards enable better AI oversight: Open Radio Access Networks (Open RAN) allow data collection across all network layers, which not only improves AI model development but also creates opportunities to audit and understand how AI systems make energy efficiency recommendations.
Multiple factors influence network energy use: The research identifies several key parameters affecting energy consumption—including airtime, data throughput, buffer status, and resource allocation—suggesting that responsible energy optimization requires understanding complex interactions rather than relying on single metrics.
Explainability supports sustainable technology goals: By integrating XAI into energy-efficient network design, the work shows how transparency can help achieve environmental objectives, allowing stakeholders to verify that AI-driven energy savings are genuine and not simply shifting consumption elsewhere in the system.
Interpretability enables better governance of AI in critical infrastructure: Understanding which RAN parameters XAI identifies as important allows network operators, regulators, and engineers to validate whether AI recommendations align with broader network reliability and sustainability goals before deployment.

Abstract

Accepted at FNFW 2024

Integrating Explainable AI for Energy Efficient Open Radio Access Networks

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Integrating Explainable AI for Energy Efficient Open Radio Access Networks

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links