Antenna Impedance Sensing for Hand Grip Blockage Detection
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Solution Overview
Problem
Mobile device antennas experience performance degradation due to blockages caused by various hand grips, leading to impedance mismatch and power loss, which existing technologies fail to accurately detect and compensate for.
Innovation Solution
A method involving sensors to detect the real and imaginary parts of antenna impedance, generating sensing signals, and inputting these into a machine learning model to output user scenarios, which includes antenna selection, tuning, and power control to mitigate blockage effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If antenna blockage detection is not implemented, then device complexity remains low, but antenna performance and reliability deteriorate due to impedance mismatch and power loss
Solution Approach 1:
The antenna system performs self-diagnosis by measuring its own impedance characteristics. The detection mechanism is integrated into the antenna circuit itself, allowing the system to automatically identify blockage conditions without requiring external detection equipment or complex additional hardware.
Solution Approach 2:
The system continuously monitors impedance parameters and uses this feedback to detect blockage conditions. By comparing measured impedance values against expected ranges, the system can identify when an antenna is blocked and adjust its operation accordingly, creating a closed-loop control system that maintains reliability.
2Measurement precision
If multiple sensors and machine learning models are added to detect user scenarios, then measurement precision improves, but device complexity increases
Solution Approach 1:
The detection system is divided into multiple independent sensing components, each responsible for measuring specific physical quantities (acceleration, gravity, proximity). These segmented sensors work together to provide comprehensive user scenario detection, allowing the system to achieve high measurement precision through coordinated operation of simpler individual components.
Solution Approach 2:
The system dynamically selects and activates specific sensor combinations and machine learning models based on the current operational context and detected conditions. This dynamic approach allows the system to maintain high measurement precision while managing complexity by not continuously running all sensors and models at full capacity.
3Measurement precision
If real-time impedance detection and machine learning processing are performed, then user scenario recognition accuracy improves, but energy consumption increases
Solution Approach 1:
Instead of continuous real-time processing, the system performs impedance detection and machine learning analysis at periodic intervals or triggered by specific events. This periodic approach maintains adequate detection accuracy for blockage identification while significantly reducing the average energy consumption compared to continuous processing.
Solution Approach 2:
The system pre-processes sensor data and performs preliminary analysis before triggering full machine learning model execution. By preparing data in advance and only running computationally intensive processing when necessary, the system maintains high detection accuracy while minimizing energy consumption during normal operation.
Data Source
AI summary
A method for generating a user scenario of an electronic device includes detecting a real part and an imaginary part of an input impedance of each antenna of the electronic device, using a plurality of sensors of the electronic device to generate a plurality of sensing signals, and entering at least the real part and the imaginary part of the input impedance of each antenna, and the plurality of sensing signals to a machine learning model to output the user scenario.