AI Network Handover Feedback for Energy-Saving Node Sleep
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Solution Overview
Problem
The increasing complexity and data usage in 3GPP networks lead to significant energy consumption challenges, necessitating efficient energy-saving mechanisms.
Innovation Solution
Implementing AI-based models to optimize network operations and reduce energy consumption by dynamically adjusting network functions and resource allocation based on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network functions and resources are continuously activated to maintain service quality, then network performance and reliability are improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic network slicing where slice configurations, resource allocations, and activation states are continuously adjusted based on real-time demand patterns. Network slices can be activated or deactivated dynamically, and resource allocation within slices adapts to changing traffic requirements, ensuring service quality is maintained only when needed while reducing energy consumption during low-demand periods.
Solution Approach 2:
The system changes operational parameters such as network slice activation states, resource allocation levels, and configuration settings based on monitored performance metrics and demand conditions. By adjusting these parameters dynamically rather than maintaining fixed high-performance settings, the network achieves energy savings while preserving service quality through adaptive parameter optimization.
2Loss of energy
If AI-based models continuously optimize network operations, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces AI-based models as intermediary components that process network operational data and generate optimization recommendations. These models act as mediators between raw network data and control decisions, automatically analyzing patterns and determining optimal resource allocation and slice configurations without requiring manual intervention, thus improving energy efficiency while managing complexity through automated intelligence.
Solution Approach 2:
The system implements self-service optimization where AI models continuously analyze network performance data and automatically adjust network slice configurations and resource allocations. The network essentially optimizes itself without external control, with the AI models serving as autonomous agents that make real-time decisions to improve energy efficiency while adapting to changing conditions.
Data Source
AI summary
An apparatus and system of providing energy saving in a network are described. A target node receives, from a source node, a handover request to handover a UE due to at least one of entry of the source node into a power saving state or mobility optimization. In response, the target node transmits a relative energy efficiency value of Bits per Joule and periodic feedback related to a status of the UE for training an AI/ML energy saving model and stops the feedback in response to a stop parameter being met. The stop parameter is a first of: a predicted period of the source node to stay in the power saving state or time for a UE to connect to the target node, a percentage of UEs handed over that enter an idle or inactive state, and a percentage of UEs handed over that are further handed to another node.