Predictive Power Control for Wi-Fi Access Point Functionality

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Solution Overview

Problem

Existing Wi-Fi networks operate at maximum potential even when not fully utilized, leading to unnecessary energy consumption and costs due to reactive power saving strategies.

Innovation Solution

Implementing machine learning models for proactive power control of access points (APs) based on collected telemetry data, including environmental and network usage data, to predict and adjust power consumption accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If access points operate at maximum potential to ensure adequate service in worst case scenarios, then network reliability is improved, but energy consumption increases

Engineering Contradiction:

Improvenetwork reliabilityVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent implements dynamic power adjustment of access points based on real-time network conditions and predictive analytics. Instead of static maximum operation, the system continuously adapts AP power levels according to actual usage patterns, environmental factors, and predicted future demand, resolving the contradiction between maintaining reliability and reducing energy consumption

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses machine learning models to predict future network usage patterns and proactively adjusts access point power levels in advance. By analyzing historical data, environmental conditions, and usage trends, the system prepares optimal power configurations before peak usage occurs, ensuring reliability while minimizing energy waste during low-demand periods

Inventive Principle:

Principle #10Preliminary action

2Productivity

If access points are operated at full functionality to accommodate maximum users, then network capacity is improved, but operational costs increase

Engineering Contradiction:

Improvenetwork capacityVSAvoidoperational costs

Core Design Contradiction:

ProductivityVSUse of energy by stationary object

Solution Approach 1:

The patent systematically changes operational parameters of access points including power levels, channel configurations, and functional capabilities based on actual network demand. By dynamically adjusting these parameters rather than maintaining fixed maximum settings, the system optimizes the balance between network capacity and operational costs, scaling resources to match actual usage

Inventive Principle:

Principle #35Parameter changes

3Loss of energy

If reactive power saving strategies are used, then energy consumption is reduced, but network service quality deteriorates

Engineering Contradiction:

Improveenergy consumptionVSAvoidnetwork service quality

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The patent implements continuous feedback loops that monitor network performance, user experience, and service quality metrics in real-time. This feedback informs dynamic power adjustment decisions, ensuring that energy-saving actions do not compromise service quality thresholds. The system learns from feedback to refine its power management strategy, maintaining quality while reducing energy consumption

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250301402A1Optimization of functionality in access point devices based on machine learning, including optimization of power control in access point devices

Publication Date: 2025.09.25 ARISTA NETWORKS INC

AI summary

Systems and methods for the proactive control and management of power usage of APs in a network are disclosed. Embodiments of such systems and methods can train a machine learning model for power control of APs in the network based on telemetry data from those APs. That machine learning model can be utilized to generate predictions associated with power control of the APs in the network such that those power control predictions can be used to determine power control directives associated with functionality of the APs.