Wireless Chipset Dynamic Power States for XR Timing

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Solution Overview

Problem

Wireless communication systems face challenges in entering power-saving modes due to the timing of uplink and downlink transmissions from devices like extended reality (XR) devices, which can lead to high power consumption and reduced portability.

Innovation Solution

Implementing a reinforcement learning machine learning (ML) model to determine a power schedule for wireless communications chipsets, allowing them to switch between low and higher power states based on specific parameters, such as primary frequency of operations, latency budget, and power budget, using techniques like target wake time (TWT) to optimize power usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the wireless communications system maintains continuous transmission support for XR devices, then communication reliability is improved, but power consumption increases

Engineering Contradiction:

Improvecommunication reliabilityVSAvoidpower consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent implements dynamic power scheduling that adapts the wireless communications system's operational state based on real-time transmission requirements. The system transitions between active and inactive states dynamically, adjusting power consumption levels according to the actual communication needs of XR devices while maintaining reliability when transmissions are required.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent employs periodic wake-up schedules and targeted wake time (TWT) mechanisms where the wireless communications system enters low-power sleep states between scheduled transmission windows. This periodic activation pattern reduces overall power consumption while ensuring communication reliability is maintained during designated active periods when XR device transmissions are expected.

Inventive Principle:

Principle #19Periodic action

2Use of energy by moving object

If the wireless communications system enters power-saving mode frequently, then power consumption is reduced, but transmission timing responsiveness deteriorates

Engineering Contradiction:

Improvepower consumptionVSAvoidtransmission timing responsiveness

Core Design Contradiction:

Use of energy by moving objectVSEase of operation

Solution Approach 1:

The patent uses machine learning models to predict future transmission requirements and proactively schedule wake-up times before actual transmissions occur. By anticipating when XR devices will need to communicate, the system prepares in advance, ensuring responsive transmission timing while maximizing power-saving opportunities during predictable inactive periods.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where the wireless communications system monitors actual transmission patterns and uses this information to refine power scheduling decisions. The system learns from past transmission timing data to optimize future wake-up schedules, balancing power consumption reduction with maintaining appropriate transmission responsiveness.

Inventive Principle:

Principle #23Feedback

3Use of energy by moving object

If the wireless communications system uses complex power scheduling algorithms, then power consumption optimization is improved, but system complexity increases

Engineering Contradiction:

Improvepower consumption optimizationVSAvoidsystem complexity

Core Design Contradiction:

Use of energy by moving objectVSDevice complexity

Solution Approach 1:

The patent employs machine learning models that autonomously learn and adapt to specific XR device transmission patterns without requiring complex manual configuration. The system self-optimizes power schedules by automatically analyzing transmission data and adjusting parameters, reducing the burden of system complexity while achieving effective power consumption optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent focuses on dynamically adjusting key power scheduling parameters such as wake-up intervals, active duration, and transition timing based on observed transmission patterns. By concentrating optimization efforts on these critical parameters rather than overhauling the entire system architecture, the patent achieves power consumption optimization with manageable increases in system complexity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250310889A1Dynamic patterns for active-inactive states for wireless communications

Publication Date: 2025.10.02 QUALCOMM INC

AI summary

Disclosed are systems and techniques for wireless communications. For instance, a process can include receiving an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model; determining a power schedule for a wireless communications chipset based on the one or more parameters using the reinforcement learning ML model; and determining to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.