Context-Aware Antenna Tuning for Compact Mobile Wireless Performance
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Solution Overview
Problem
Current mobile devices, such as smartwatches, are limited by their small size, which restricts the placement and efficiency of multiple antennas, leading to compromised wireless performance due to space constraints and metal enclosures, and they fail to adapt to individual user interactions and environments.
Innovation Solution
The system dynamically tunes wireless antennas based on contextual information, using AI and ML to adjust antenna operational parameters, such as beam steering and radiation patterns, in real-time to optimize performance for the user and environment, allowing for adaptive and personalized wireless functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple antennas are placed in a small mobile device, then wireless communication capability is improved, but space constraints and metal enclosures compromise antenna efficiency and performance
Solution Approach 1:
The patent implements dynamic antenna tuning by continuously monitoring contextual information (user interactions, environmental conditions, device orientation) and adjusting antenna operational parameters in real-time. This allows a single antenna system to adaptively optimize performance for different communication scenarios, effectively providing multi-antenna capability without requiring multiple physical antennas, thus resolving the space constraint while maintaining wireless communication versatility.
Solution Approach 2:
The system changes antenna operational parameters (such as impedance, resonant frequency, and radiation patterns) based on contextual conditions. By dynamically adjusting these parameters, the antenna can optimize its performance for different wireless communication requirements without changing its physical structure or requiring additional antenna elements, thereby resolving the contradiction between limited device space and needed wireless capability.
2Adaptability or versatility
If antenna parameters are fixed, then device complexity is reduced, but the device fails to adapt to individual user interactions and environments
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors contextual information including user interactions (touches, gestures), environmental conditions (temperature, humidity), and device state (orientation, motion). This feedback is processed by AI/ML models that determine optimal antenna parameters, which are then applied in real-time. This closed-loop feedback system enables adaptive optimization without requiring complex manual configuration or user intervention.
Solution Approach 2:
The antenna tuning system operates autonomously using self-service principles. AI and machine learning models pre-trained during manufacturing enable the device to automatically analyze contextual information and adjust antenna parameters without external assistance. The system performs self-diagnosis and self-optimization, reducing the need for complex external tuning equipment or expert intervention while achieving high adaptability to different users and environments.
3Speed
If AI and ML models are pre-trained during manufacturing, then processing speed is improved, but manufacturing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training AI and machine learning models during the manufacturing process. Contextual information datasets are collected and used to train models that are then embedded in the device before deployment. This pre-processing of intelligence allows the device to perform rapid real-time inference and antenna parameter adjustment without requiring complex runtime training, thus achieving fast response speed while managing manufacturing complexity through one-time model preparation.
Data Source
AI summary
The disclosed computer-implemented method may include accessing various portions of contextual information based on at least one touch input from a touch-based sensor on a mobile electronic device. The method may next include determining, based on the contextual information, which of different operational antenna parameters associated with at least one antenna of the mobile electronic device are to be changed. The method may then include changing the specified operational parameters associated with the antenna on the mobile electronic device according to the determination. Various other methods, systems, and computer-readable media are also disclosed.