Hierarchical Network Control for Load-Balanced Energy Saving
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Solution Overview
Problem
Conventional cellular networks face challenges in optimizing network energy consumption due to sub-optimal user equipment association strategies, lack of centralized load balancing frameworks, and limited scalability of AI/ML approaches, leading to inefficient energy usage and potential disruptions in service quality.
Innovation Solution
Implementing a federated learning process using graph neural networks (GNNs) for load balancing, which involves training models at both radio unit and radio access network intelligence controller levels, allowing for dynamic user equipment re-association based on energy savings criteria, leveraging O-RAN frameworks and hierarchical federated learning to optimize network energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional user equipment association strategies are used, then network operation is simple, but network energy consumption is high
Solution Approach 1:
The patent introduces a centralized load balancing framework with intelligent controllers that act as intermediaries between radio units and the network core. These controllers use federated learning to optimize energy consumption by making intelligent user equipment association decisions, thereby reducing overall network energy usage while managing the complexity through a dedicated intermediary layer.
Solution Approach 2:
The system dynamically changes association parameters based on network conditions, energy consumption metrics, and load balancing requirements. By adjusting association parameters through machine learning models trained via federated learning, the system optimizes energy consumption without requiring complete reconfiguration of the network architecture.
2Productivity
If centralized load balancing framework is implemented, then energy optimization is improved, but system complexity increases
Solution Approach 1:
The patent segments the load balancing system into hierarchical levels: centralized intelligent controllers that perform high-level optimization decisions, and distributed radio units that execute specific actions. This segmentation allows energy optimization to be improved at the centralized level while distributing implementation complexity across multiple components, preventing any single element from becoming overly complex.
Solution Approach 2:
The system adds a hierarchical dimension to the network architecture by introducing multiple levels of control (centralized controllers and distributed radio units). This dimensional change enables energy optimization to occur at the strategic level while operational complexity is managed at the tactical level, effectively separating optimization efficiency from implementation complexity.
3Productivity
If AI/ML approaches are applied, then network efficiency is improved, but scalability is limited
Solution Approach 1:
The patent implements federated learning where each radio unit and intelligent controller independently trains local machine learning models using its own data and conditions. This self-service approach allows the system to scale efficiently because each component learns autonomously without requiring centralized training of all models simultaneously, thereby improving both network efficiency and scalability.
Solution Approach 2:
The system performs preliminary local model training at each radio unit and controller before aggregating results. This preliminary action enables AI/ML approaches to be applied efficiently across the network, as each node prepares its contributions in advance, allowing the overall system to scale without proportionally increasing the complexity of centralized coordination.
4Loss of energy
If user equipment re-association is performed dynamically, then energy savings are achieved, but service quality may be disrupted
Solution Approach 1:
The patent implements feedback mechanisms where the centralized intelligent controllers continuously monitor service quality metrics and energy consumption. Based on this feedback, the system dynamically adjusts user equipment association decisions to achieve energy savings while maintaining service quality thresholds. The feedback loop ensures that re-association actions are taken only when they result in net energy benefits without degrading service quality.
Solution Approach 2:
The system performs preliminary assessments and simulations before executing user equipment re-association. By evaluating potential re-association scenarios in advance and cushioning against potential service quality degradation through careful selection of target cells and timing, the system achieves energy savings while protecting service quality from disruption.
Data Source
AI summary
Facilitating hierarchical network control for load-balanced network energy savings in advanced communication networks is discussed. A method includes based on a federated learning process, determining, by a system comprising at least one processor, a graphical representation of a communication network. The graphical representation identifies respective communications between a group of radio units and a radio access network intelligent controller. The method also includes, based on the graphical representation, facilitating, by the system, user equipment association that defines an action for a network traffic load balancing process according to a network energy savings criterion. The network traffic load balancing process transfers network traffic of a specified user equipment from a source cell of a group of cells of the communication network to a target cell of the group of cells.