Duplexer Impedance Tuning Using ML-Based Antenna Matching
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Solution Overview
Problem
Determining the impedance of an antenna in a radio frequency device is difficult, leading to inefficient transmission and reception of wireless signals due to interference between transmitter and receiver, and existing impedance tuner settings often result in overshooting or mismatched tuner impedance.
Innovation Solution
The implementation of a radio frequency device with a voltage standing wave ratio detector and machine-learning model to accurately determine antenna impedance, adjusting the impedance tuner based on measured leakage signals and using optimization algorithms to efficiently match the antenna impedance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional impedance measurement methods are used, then the device complexity is reduced, but the measurement precision of antenna impedance deteriorates
Solution Approach 1:
The patent introduces a machine-learning model as an intermediary between the impedance tuner and the antenna. The model processes multiple test impedance settings and corresponding leakage measurements to estimate antenna impedance, replacing direct measurement methods and achieving higher precision without proportionally increasing hardware complexity
Solution Approach 2:
The patent replaces traditional mechanical impedance measurement systems with a computational approach using machine-learning algorithms. The system substitutes physical measurement devices with digital processing that analyzes leakage signals through multiple test settings to determine antenna impedance, reducing hardware complexity while improving measurement accuracy
2Manufacturing precision
If existing impedance tuner settings are applied, then the ease of operation is improved, but the manufacturing precision of impedance matching deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the machine-learning model with extensive impedance data before deployment. The model is prepared in advance to quickly evaluate multiple test impedance settings and predict optimal tuning parameters, achieving high precision matching without complex real-time adjustments
Solution Approach 2:
The patent implements feedback by measuring leakage signals at multiple test impedance settings and using these measurements to train and refine the machine-learning model. The system continuously adjusts impedance tuner settings based on feedback from leakage measurements, achieving precise impedance matching while maintaining ease of operation through automated control
3Measurement precision
If multiple test impedance settings are applied, then the measurement precision of antenna impedance is improved, but the loss of time increases
Solution Approach 1:
The patent applies periodic action by systematically cycling through multiple predetermined test impedance settings in a structured sequence. The machine-learning model evaluates each setting rapidly and uses the collected data to estimate antenna impedance, achieving high precision measurement while minimizing total tuning time through optimized measurement cycles
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient and accurate impedance matching, reducing interference and improving isolation performance between transmitter and receiver, ensuring optimal signal transmission and reception.
Implementation Method 1
The voltage standing wave ratio detector determines an antenna impedance at the antenna
Data Source
AI summary
This disclosure provides techniques for impedance matching. A radio frequency (RF) device includes a power detector to determine a transmitter leakage and a post-processing unit to determine a receiver leakage, and determines if isolation is acceptable based on the leakages. The RF device may include a device for measuring antenna impedance. Otherwise, the RF device may select multiple tuner settings (e.g., capacitor values) for test signals to be transmitted and received at a target frequency, determine multiple sets of leakage values, determine multiple reflection coefficients based on the multiple sets of leakage values, and determine an estimated antenna impedance at the target frequency based on the reflection coefficients. The RF device then determines impedance tuner settings based on the measured or estimated antenna impedance. Alternatively, the RF device determines impedance tuner settings using an inverse machine-learning model based on a determined matching impedance.