Page 345 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

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Item Details
Role of Standards 3GPP standards for 5G network management, ITU-T Recommendations
on energy efficiency in telecommunications. 4.3 - 5G
Open Source Yes, we intend to release the core algorithms and SDN controller modules
as open-source.

Commercial Utiliza- The solution can be utilized and deployed commercially in 5G network
tion infrastructure.

2 Use Case Description

2�1 Description

5G networks significantly increase energy consumption compared to 4G, contributing to the
ICT sector’s growing electricity usage, estimated at 7% globally [1]. A single 5G macro base
station may consume ~4.3 kW, nearly 4× that of a 4G site (~1.1 kW), highlighting the urgency
of efficient network planning [4]. Traditional energy-saving methods, like shutting down
underutilized links, often compromise network performance and fail to respond to dynamic
traffic patterns, reducing overall efficiency and increasing carbon emissions. To address these
challenges, AI-driven, SDN-enabled solutions are emerging as powerful tools. Organizations
like ITU advocate for intelligent planning using big data and machine learning to predict traffic
and adapt network behavior in real-time. SDN controllers can dynamically reroute traffic and
control base station power states to optimize energy use without degrading service [3]. Our
proposed solution adopts a dynamic max-min planning framework for energy-efficient 5G
backhaul. The framework balances network connectivity and throughput while minimizing
energy consumption by dynamically adjusting network topology and forwarding paths based
on traffic demands.

Key Components:
• Traffic Prediction: We leverage historical data and machine learning models to forecast
traffic across time slots, enabling proactive planning.
• Topology Adjustment: Based on predictions, the framework activates/deactivates network
links to reduce energy usage while maintaining end-to-end connectivity.
• Max-Min Optimization: The planning problem is formulated to maximize network flow
while minimizing energy, ensuring scalable and efficient resource allocation.
• H-CRAN Architecture: By separating control and data planes, Heterogeneous Cloud RAN
(H-CRAN) supports flexible control, ideal for implementing our framework.
• Distributed SDN Control: A multi-tier SDN controller hierarchy enhances scalability,
reliability, and localized decision-making.

Technical Implementation:

• Prediction Algorithm: We will implement a time series or ML-based model to predict
end-to-end traffic demands.
• Optimization Engine: The routing problem is modeled as a multi-objective graph
optimization task using Max-Min fairness principles, where the aim is to maximize the
minimum link bandwidth utilized while minimizing total energy cost. This is dynamically
recalculated as topology changes based on traffic prediction.

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