DU Core Power-State Control for Wireless Traffic Spikes
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Solution Overview
Problem
Managing power consumption efficiently in network entities with multiple processing cores is challenging due to increased traffic loads and UE connections, leading to suboptimal energy efficiency and network performance.
Innovation Solution
A method and system for dynamic resource optimization in distributed units (DUs) that transition processing cores based on contextual traffic parameters, including current and predicted limits of RRC-connected UEs and PRBs, using machine learning to predict traffic trends and adjust power states accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of processing cores is increased to handle substantial traffic load, then network capacity and reliability are improved, but power consumption increases
Solution Approach 1:
The patent implements dynamic power state management where processing cores can transition between active and low-power states based on real-time traffic conditions. The system monitors parameters like number of active UEs and PRB utilization to dynamically adjust the power state of processing cores, allowing the network to maintain reliability during high-load periods while reducing power consumption during low-activity periods.
Solution Approach 2:
The system changes operational parameters of processing cores by transitioning them between different power states (e.g., active, idle, low-power) based on traffic conditions. This parameter change allows the same hardware infrastructure to operate at different power consumption levels without compromising network capacity when needed.
2Reliability
If all processing cores remain active to maintain network performance, then service quality is preserved, but energy efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts the operational state of processing cores based on monitored traffic parameters such as number of active UEs and PRB utilization. When traffic conditions indicate low demand, the system transitions excess cores to low-power states while maintaining sufficient active cores to handle current load, thereby improving energy efficiency without compromising network performance.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors traffic parameters (number of active UEs, PRB utilization) and uses this information to adjust processing core power states. This closed-loop control ensures that power management decisions are based on actual network conditions, maintaining performance while optimizing energy efficiency.
3Use of energy by moving object
If processing cores are transitioned to low-power states to save energy, then power consumption is reduced, but response time increases
Solution Approach 1:
The system dynamically manages power states based on predicted and actual traffic conditions. By using machine learning models to forecast traffic patterns, the system can proactively transition cores to low-power states when future load is expected to be low, while maintaining the ability to quickly reactivate cores when traffic spikes occur, thus balancing power savings with response time requirements.
Solution Approach 2:
The patent uses machine learning models to predict future traffic conditions and proactively adjusts processing core power states in advance based on these predictions. This preliminary action allows the system to optimize power consumption during predictable low-load periods while ensuring cores are ready to respond immediately when traffic conditions change.
Data Source
AI summary
Embodiments disclosed herein provide a method and system for sending current traffic parameters pertaining to a Distribution unit (DU) comprising multiple processing cores, to a network entity. Further, in response to sending the current traffic parameters, the DU receives a first limit of Radio Resource Control (RRC)-connected User Equipments (UEs) and second limit of Physical Resource Blocks (PRBs), from the network entity. Furthermore, based on the first limit and the second limit, the DU transitions a set of processing cores, from the plurality of processing cores, from a first power state to a second power state.