Adaptive Target Wake Time for Low-Latency Wireless Power Saving
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Solution Overview
Problem
Existing wireless communication systems face challenges in balancing power consumption and latency requirements, particularly in battery-operated devices with applications like augmented reality/virtual reality (AR/VR) that demand low latency and efficient power management.
Innovation Solution
Implementing prediction-based latency aware power saving techniques using adaptive target wake time (TWT) to align data transmission/reception times across different applications, allowing devices to enter deeper power save modes and reduce wake-ups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the device wakes up frequently to handle data from multiple applications, then latency requirements are met, but power consumption increases
Solution Approach 1:
The device performs preliminary actions by predicting future wakeup times based on historical data from multiple applications before actually needing to wake up. The system calculates predicted wakeup times using transmit times and latency values from different applications, then enters power save mode until these predicted times, avoiding unnecessary wakeups while ensuring data is ready when needed.
Solution Approach 2:
The system dynamically adjusts the wakeup schedule by aligning data transmission/reception times across different applications. The predicted wakeup time is determined by comparing adjusted transmit times from multiple applications and selecting the appropriate time to wake, making the wakeup pattern adaptive rather than static or periodic.
2Use of energy by moving object
If the device enters deeper power save modes, then power consumption decreases, but latency may increase
Solution Approach 1:
The system performs preliminary calculation of predicted wakeup times using data from multiple applications before entering deep power save mode. By determining the alignment of data transmission times from different applications in advance, the device can confidently enter deeper power save modes knowing when to wake up to meet latency requirements.
Solution Approach 2:
The system uses feedback from End of Service Period (EOSP) signals and historical transmission data to refine predicted wakeup times. The wireless device monitors actual wakeup needs and adjusts future predicted wakeup times accordingly, ensuring latency requirements are met while maximizing power save depth.
3Use of energy by moving object
If the device aligns data transmission times across applications, then power save efficiency improves, but coordination complexity increases
Solution Approach 1:
The device performs preliminary alignment by calculating adjusted transmit times for multiple applications using their respective latency values before data transmission. The predicted wakeup time is determined in advance by comparing these adjusted times, allowing the device to coordinate multiple applications without complex real-time synchronization during active periods.
Solution Approach 2:
The system uses self-service by having each application's data characteristics (transmit time and latency value) contribute automatically to the predicted wakeup time calculation. The device serves itself by internally aligning the data from different applications without requiring external coordination or complex inter-application communication protocols.
4Productivity
If the device wakes up at predicted wakeup time, then data transmission is synchronized, but timing precision requirements increase
Solution Approach 1:
The system performs preliminary timing calculations by determining predicted wakeup times based on adjusted transmit times from multiple applications. The predicted wakeup time is calculated in advance using the application's transmit time and latency value, allowing the device to synchronize data transmission without requiring extremely precise real-time timing adjustments during wakeup.
Data Source
AI summary
This disclosure provides methods, components, devices, and systems for prediction-based latency aware power saving using adaptive target wake time (TWT). Some aspects more specifically relate to aligning traffic flows from multiple applications by using next expected times for packet transmissions, latency threshold values, processing times, and statistics to determine a next TWT. The next TWT may be communicated from a wireless device to a companion device. The wireless device may enter a sleep mode until the next TWT.


