5G Node Power Scheduling Using Mobility Pattern Prediction

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Solution Overview

Problem

5G mobile networks face complex data usage patterns that complicate the identification of periods when transmitters can be powered down to optimize power consumption without disrupting network service.

Innovation Solution

Implement a system with connectivity and mobility analytics trackers to monitor association and mobility patterns, using machine learning to generate power saving schedules for 5G nodes, allowing transmitters to be powered down during low-demand periods while maintaining network connectivity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If 5G transmitters are automatically powered down when no data is being transmitted, then power consumption is improved, but network service reliability deteriorates due to complex data usage patterns

Engineering Contradiction:

Improvepower consumptionVSAvoidnetwork service reliability

Core Design Contradiction:

Use of energy by moving objectVSReliability

Solution Approach 1:

The system performs preliminary actions by continuously monitoring data usage patterns and predicting future transmission needs before actually shutting down transmitters. The machine learning model analyzes historical data to forecast when transmitters can be safely powered down, ensuring network reliability is maintained while achieving power savings.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring network traffic patterns and using this information to dynamically adjust transmitter power states. The machine learning model receives ongoing data about network usage and adjusts predictions accordingly, creating a closed-loop system that balances power consumption with service reliability.

Inventive Principle:

Principle #23Feedback

2Reliability

If all 5G mobile network stations remain powered on during reduced transmissions, then network service reliability is maintained, but power consumption increases unnecessarily

Engineering Contradiction:

Improvenetwork service reliabilityVSAvoidpower consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by powering down only a subset of 5G stations rather than all stations. The machine learning model identifies which specific transmitters can be safely shut down based on real-time network conditions and data usage patterns, allowing partial power reduction while maintaining overall network reliability.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes the operational parameters of 5G stations dynamically. Instead of maintaining a fixed power state, the machine learning model adjusts power consumption parameters based on predicted network needs, transitioning stations between active and powered-down states according to learned patterns in data usage.

Inventive Principle:

Principle #35Parameter changes

3Use of energy by moving object

If machine learning models are used to predict power saving opportunities, then power consumption is optimized, but device complexity increases

Engineering Contradiction:

Improvepower consumptionVSAvoidsystem complexity

Core Design Contradiction:

Use of energy by moving objectVSDevice complexity

Solution Approach 1:

The system implements self-service by using machine learning models that automatically learn and adapt to network patterns without manual intervention. The model continuously trains on incoming data and autonomously makes decisions about when to power down transmitters, reducing the need for complex manual configuration and management.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The machine learning model serves multiple functions: it predicts network traffic patterns, identifies power saving opportunities, and coordinates transmitter shutdown decisions. This multi-functionality reduces overall system complexity by consolidating multiple specialized components into a single versatile predictive system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12604278B25G inter- and intra-network node coordination for power consumption optimization and reduction

Publication Date: 2026.04.14 CISCO TECHNOLOGY INC

AI summary

Association and mobility patterns corresponding to client devices within a 5G network are tracked in real-time. A machine learning model is trained to identify, based on these patterns, periods of time for powering down one or more 5G nodes within the 5G network. The machine learning model, based on these periods of time, generates a set of power saving profiles that are used to automatically define power saving modes for the one or more 5G nodes. The machine learning model is updated according to changes to the association and mobility patterns resulting from the power saving modes.