Predictive Wireless Link Selection for Mobile Devices
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Solution Overview
Problem
Current radio resource management systems for mobile devices lack the ability to adaptively select optimal wireless links based on predicted paths and usage patterns, leading to inefficient energy consumption and variable quality of service.
Innovation Solution
A context-aware radio resource management system that predicts future mobile device paths and usage patterns, utilizing wireless intelligence reports and user profiles to determine the optimal wireless link selection by assessing radio frequency conditions, energy consumption, and quality of service requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional radio resource management systems are used, then device complexity is reduced, but energy consumption efficiency deteriorates and quality of service becomes variable
Solution Approach 1:
The system performs path prediction and wireless link assessment in advance before the mobile device actually moves to future locations. By predicting the device's movement trajectory and pre-assessing radio frequency conditions, energy consumption, and quality of service at future locations, the system prepares optimized wireless link selections ahead of time. This preliminary action allows the device to switch to pre-determined optimal links without real-time computation delays, improving energy efficiency while managing complexity through advance preparation.
Solution Approach 2:
The radio resource management system autonomously predicts mobile device paths using historical location data and movement patterns, then self-selects optimal wireless links without continuous user input or manual configuration. The system serves itself by automatically assessing radio conditions, evaluating energy consumption trade-offs, and determining quality of service requirements based on predicted usage patterns at future locations. This self-service capability improves energy efficiency through automated optimization while the system manages its own complexity internally.
2Reliability
If dynamic wireless link selection based on predicted paths is implemented, then quality of service is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system predicts wireless link quality, energy consumption, and quality of service requirements at future locations along the predicted device path before the device actually arrives there. By performing these assessments in advance, the system identifies optimal wireless links ahead of time, ensuring high quality of service when needed without requiring complex real-time decision-making. This preliminary assessment approach improves reliability while managing computational complexity through time-shifting the computational burden.
Solution Approach 2:
The system continuously monitors actual device location, movement patterns, and wireless link performance, then uses this feedback to refine path predictions and wireless link assessments. The feedback loop compares predicted versus actual device behavior, allowing the system to adjust its predictions and re-assess optimal links dynamically. This feedback mechanism improves quality of service by adapting to changing conditions while managing complexity through iterative refinement rather than complete re-computation.
3Adaptability or versatility
If real-time wireless link assessment is performed, then adaptability is improved, but energy consumption increases
Solution Approach 1:
Instead of continuously assessing wireless links in real-time as the device moves, the system performs link assessments in advance at predicted future locations along the device's trajectory. By shifting the assessment timing to before the device actually needs the connection, the system maintains adaptability to path changes while avoiding the continuous energy expenditure of real-time monitoring. The pre-assessed links are then activated when needed, reducing overall energy consumption while preserving adaptability.
Solution Approach 2:
The system performs wireless link assessments at periodic intervals based on predicted device movement and location changes, rather than continuously monitoring all possible links at all times. By assessing links periodically at strategically chosen future locations along the predicted path, the system maintains adaptability to changing conditions while significantly reducing energy consumption compared to continuous real-time assessment. This periodic approach balances adaptability with energy efficiency.
Data Source
AI summary
An information handling system includes a storage device for storing a spatial-temporal radio frequency profile for indicating signal quality for wireless links available at the location. The information handling system further includes an application processor that selects a predicted future path for a mobile information handling system during a future time interval. The application processor determines predicts radio quality of wireless links over the predicted future path selected for the system based on measured radio quality of service parameters for locations.