Sleep-Mode Statistics for Wireless Power Optimization
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Solution Overview
Problem
Maximizing sleep-mode intervals in wireless mobile devices without degrading the quality and performance required by real-time services is a challenge, as existing technologies struggle to balance power conservation with service quality.
Innovation Solution
A system and method that capture and utilize sleep-mode statistics to optimize power-saving mechanisms by tracking sleep and listening windows, pending MAC SDUs, and adjusting power-saving class parameters, enabling efficient power management and performance monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If sleep-mode intervals are extended to maximize power conservation, then power consumption is reduced, but service quality and performance of real-time services deteriorate
Solution Approach 1:
The system dynamically adjusts sleep-mode parameters based on real-time traffic conditions and service requirements. The BS monitors traffic patterns and MS behavior to adaptively modify sleep and listening window configurations, allowing the system to optimize between power savings and service quality based on current conditions rather than using fixed parameters
Solution Approach 2:
The system implements feedback mechanisms where the BS captures and analyzes sleep-mode statistics including MS availability, traffic patterns, and service performance metrics. This feedback is used to continuously refine and adjust power-saving parameters, ensuring that sleep-mode operation does not degrade real-time service quality while maximizing power conservation
2Duration of action of stationary object
If sleep-mode intervals are extended to conserve power, then battery life is improved, but data transmission reliability deteriorates
Solution Approach 1:
The system performs preliminary actions by buffering MAC SDUs at the BS before the MS enters sleep mode. The BS prepares and queues data transmissions in advance, ensuring that data is ready to be transmitted immediately when the MS becomes available, thereby maintaining transmission reliability while enabling extended sleep periods
Solution Approach 2:
The system dynamically adjusts the balance between sleep duration and data buffering based on traffic characteristics and MS availability patterns. For real-time services with strict reliability requirements, the system reduces sleep intervals or increases buffering capacity, while for non-real-time traffic, longer sleep periods are permitted
3Use of energy by moving object
If power-saving mechanisms are optimized to extend sleep windows, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where the MS autonomously determines its sleep and listening window timings based on pre-negotiated parameters. The MS independently manages its own power-saving operation without requiring complex centralized control, reducing overall system complexity while maintaining energy efficiency
Solution Approach 2:
The system optimizes energy efficiency by carefully adjusting key parameters such as sleep window duration, listening window timing, and interval between sleep cycles. These parameter changes enable extended sleep modes while maintaining manageable system complexity through focused optimization of critical control variables
Data Source
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AI summary
Embodiments of sleep-mode statistics apparatus, systems, and methods are described generally herein. Other embodiments may be described and claimed.