Cellular Network Sleep Management via Pre-warming
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Solution Overview
Problem
Current cellular communication network sleep state management approaches lead to noticeable network performance degradation due to delayed cell wake-up times, which can result in service disruptions during sudden traffic increases, and cautious configurations to mitigate this often reduce energy savings.
Innovation Solution
A central network automation platform that identifies sleep-eligible cells, deploys customized sleep feature activation parameters, and monitors performance to dynamically manage transitions between sleep and active states, ensuring energy savings without significant performance impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If cells are configured to enter sleep state when traffic is low, then energy consumption is reduced, but network performance degrades during sudden traffic increases due to five-minute wake-up delay
Solution Approach 1:
The system performs preliminary actions by pre-warming cells before actual traffic arrival. When traffic patterns indicate upcoming demand, the system proactively transitions cells from sleep to active state in advance, so they are ready to immediately handle traffic without the five-minute delay. This anticipatory approach maintains energy savings during low traffic while ensuring performance readiness before traffic spikes occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring traffic patterns, cell performance metrics, and energy consumption data. Based on this feedback, the system dynamically adjusts sleep state configurations, wake-up thresholds, and pre-warming timing. This closed-loop control enables the system to learn from past traffic patterns and optimize the balance between energy savings and performance reliability in real-time.
2Loss of energy
If cells spend more time in sleep state to maximize energy savings, then energy consumption decreases, but the ability to respond to sudden traffic demands is reduced
Solution Approach 1:
The system applies dynamics by making sleep state configurations adaptive rather than static. It dynamically adjusts the duration and frequency of sleep states based on real-time traffic conditions, historical patterns, and predicted demand. The system can flexibly modify wake-up thresholds and pre-warming parameters to optimize the balance between energy savings and traffic handling capability under varying operational conditions.
Solution Approach 2:
The system utilizes parameter changes by modifying multiple configurable parameters including sleep threshold values, wake-up triggers, pre-warming duration, and transition timing. By dynamically adjusting these parameters based on traffic patterns and network conditions, the system optimizes the trade-off between energy savings and productivity, allowing cells to sleep longer when appropriate while maintaining rapid response capability when needed.
3Reliability
If cautious sleep state configurations are used to reduce performance degradation, then network reliability improves, but energy savings are reduced
Solution Approach 1:
The system introduces an intermediary pre-warming mechanism that acts as a buffer between sleep state and full active state. Instead of directly transitioning from deep sleep to full operational mode, the system uses an intermediate pre-warmed state that takes effect before traffic arrival. This intermediary approach allows more aggressive sleep configurations while maintaining performance reliability through the pre-prepared intermediate state.
Data Source
AI summary
The described technology is generally directed towards cellular communication network sleep management. A central network automation platform can control sleep settings deployed to radio access network nodes that support the cells of the cellular communication network. The network automation platform can collect cell configuration data and can use the cell configuration data to determine sleep settings for sleep-eligible cells. The network automation platform can furthermore monitor performance of the sleep-eligible cells and neighbor cells to determine whether the sleep settings have led to degraded performance.