Wi-Fi TWT Parameter Adaptation via Traffic Pattern State Machine
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Solution Overview
Problem
Existing wireless communication systems struggle to optimize power consumption in Wi-Fi stations due to the lack of effective methods for detecting traffic types and adapting Target Wake Time (TWT) parameters accordingly.
Innovation Solution
A system and method that utilize statistical information of incoming network traffic to classify traffic patterns using a state machine, and then adapt TWT parameters to optimize power consumption. The traffic patterns include bursty, random, and stable traffic types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If TWT parameters are fixed without traffic pattern adaptation, then device complexity is reduced, but power consumption cannot be optimized for different traffic types
Solution Approach 1:
The patent implements dynamic TWT parameter adaptation by introducing a state machine that continuously monitors traffic patterns and adjusts TWT parameters in real-time. The system transitions between different operational states (e.g., idle, active, sleep states) based on detected traffic characteristics, enabling power consumption optimization without requiring fixed parameters. This dynamic approach resolves the contradiction by making the system adaptable to varying traffic conditions while maintaining manageable complexity through structured state transitions.
Solution Approach 2:
The patent changes TWT parameters (such as TWT wake interval, TWT service period duration) based on detected traffic patterns. By monitoring statistical information of incoming network traffic and classifying it into different patterns (e.g., periodic, bursty, background traffic), the system adjusts TWT parameters to match the traffic characteristics. This parameter adaptation enables optimal power consumption for each traffic type while using a standardized detection mechanism to control complexity.
2Use of energy by moving object
If TWT parameters are adapted dynamically based on traffic patterns, then power consumption is optimized, but device complexity increases
Solution Approach 1:
The patent segments the traffic analysis function into distinct components: statistical information collection, traffic pattern classification using a state machine, and TWT parameter adaptation. This segmentation allows each component to be independently optimized and managed. The state machine itself is segmented into discrete states and transitions, making the complexity manageable and traceable. By dividing the overall function into modular segments, the system achieves dynamic power optimization without overwhelming complexity.
Solution Approach 2:
The patent implements a feedback loop where the state machine continuously monitors traffic patterns and provides feedback to adjust TWT parameters. The system observes traffic characteristics, classifies them into patterns, and uses this classification feedback to select appropriate TWT parameters. This closed-loop feedback mechanism enables automatic adaptation to traffic conditions, optimizing power consumption while keeping the complexity contained within the feedback control structure rather than requiring complex manual configuration.
3Measurement precision
If statistical information collection and traffic classification are implemented, then accurate TWT parameter adaptation is achieved, but processing overhead increases
Solution Approach 1:
The patent applies partial action by collecting and analyzing only the essential statistical information needed for traffic pattern classification, rather than examining all possible traffic characteristics. The state machine focuses on identifying key traffic patterns (periodic, bursty, background) using a limited set of metrics such as packet arrival intervals and traffic volume. This selective analysis achieves sufficient detection accuracy for TWT parameter adaptation while minimizing processing overhead and time consumption.
Data Source
AI summary
A method includes obtaining statistical information of incoming network traffic. The method also includes using a state machine to classify the incoming network traffic as a traffic pattern based on the statistical information. The method further includes using the traffic pattern to adapt one or more Target Wakeup Time (TWT) parameters to optimize power consumption of a Wi-Fi station. The traffic pattern includes at least one of bursty traffic, random traffic, and stable traffic.