Multi-Core Processor Frequency Scaling for Base Station Energy Use
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Solution Overview
Problem
In wireless communication networks, base stations consume excessive energy due to constant operation at maximum processor frequency, leading to inefficient energy utilization during off-peak hours when traffic is lower.
Innovation Solution
A method and system for dynamic frequency scaling of multi-core processors in wireless communication networks, involving a network node transmitting core-load data and key indicators to a central management entity, which builds a core-load prediction model to determine an optimum processor frequency based on estimated core-load data, reducing processing at the node and enabling adaptive frequency adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the base station runs at maximum processor frequency all the time, then processing capability is ensured, but energy consumption increases
Solution Approach 1:
The processor frequency is made dynamic rather than static. The system continuously monitors core-load data and adjusts the processor frequency in real-time based on actual traffic conditions. During peak hours, the frequency increases to maximum to ensure processing capability, while during off-peak hours, it reduces to lower frequencies to save energy, thus resolving the contradiction between maintaining processing capability and reducing energy consumption.
Solution Approach 2:
The system changes the operating parameter (processor frequency) based on varying conditions. By monitoring core-load data and traffic patterns, the system adjusts the frequency parameter dynamically - increasing it when high processing capability is needed and decreasing it when energy efficiency is prioritized, thereby resolving the contradiction between reliability and energy consumption.
2Use of energy by stationary object
If the processor frequency is reduced during off-peak hours, then energy consumption decreases, but processing capability is compromised
Solution Approach 1:
The system implements feedback control by continuously monitoring core-load data and traffic conditions. When traffic increases during previously low-traffic periods, the feedback mechanism detects this change and automatically increases the processor frequency to maintain processing capability. This feedback loop ensures that energy efficiency is optimized during off-peak hours while processing capability is maintained when needed.
Solution Approach 2:
The processor frequency transitions from a static reduced state to a dynamic adjustable state. The system can quickly scale the frequency up or down based on real-time traffic conditions, ensuring that energy efficiency is achieved during off-peak hours while processing capability is restored immediately when traffic demands increase, thus resolving the contradiction between energy efficiency and processing capability.
3Use of energy by stationary object
If core-load prediction model is implemented, then energy optimization is achieved, but system complexity increases
Solution Approach 1:
The core-load prediction model acts as an intermediary between raw traffic data and processor frequency control. Instead of directly controlling frequency based on instantaneous load, the prediction model processes and predicts future core-load trends, providing optimized frequency recommendations. This intermediary layer enables energy optimization through accurate predictions while managing system complexity by using standardized machine learning algorithms that can be integrated into existing network management architectures.
Data Source
AI summary
The disclosure describes a method and system for dynamic frequency scaling of multi-core processor in wireless communication networks. The method comprising: transmitting, by a network node (NN), core-load data and a plurality of key indicators of each core group of a plurality of core groups in the multi-core processor to a central management entity (CME); receiving, by the NN, a core-load prediction model associated for each core group from the CME; determining, by the NN, an estimated core-load data for each core group using the associated core-load prediction model and determining, by the NN, a maximum estimated core-load data among the estimated core-load data of each core group; and determining, by the NN, an optimum multi-core processor frequency for the network node based on the maximum estimated core-load data.