Tunable Antenna Phase Configuration via AI Model
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Solution Overview
Problem
Existing tunable antenna systems rely on pre-calibrated pointing angle-code tables, which require large storage space, limit precision, and do not fully utilize the continuous adjustability of phase shifters, leading to suboptimal performance.
Innovation Solution
A tunable antenna control method using an artificial intelligence model, specifically an auto-encoder, to calculate phase-configuration parameters directly from beam pointing angles, eliminating the need for pre-calibrated tables and enabling continuous phase adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-calibrated pointing angle-code tables are used for phase configuration, then the system has simple control logic, but storage space requirements increase and measurement precision is limited
Solution Approach 1:
The patent extracts the phase configuration data from static pre-calibrated tables and replaces it with a dynamic calculation mechanism using an auto-encoder neural network. The model learns the mapping relationship between pointing angles and phase configuration parameters during training, then directly computes optimal phase configurations during operation, eliminating the need for large storage tables while maintaining or improving precision through continuous adjustment capability
Solution Approach 2:
The patent replaces the mechanical lookup table system with an intelligent computational system. Instead of storing predetermined phase values in tables and performing discrete lookups, the system uses an trained auto-encoder model that continuously calculates optimal phase configurations based on real-time pointing angle inputs, enabling smoother and more precise beam control
2Adaptability or versatility
If pre-calibrated pointing angle-code tables are used, then the system structure is simple, but the continuous adjustability of phase shifters is not fully utilized
Solution Approach 1:
The patent transforms the static phase configuration approach into a dynamic system. The auto-encoder model continuously adapts phase configuration parameters based on real-time pointing angle inputs, allowing the system to fully utilize the continuous adjustability of phase shifters. The model learns optimal phase configurations across the entire operating range during training, enabling smooth and precise beam steering without the discretization limitations of lookup tables
Solution Approach 2:
The patent changes the control parameters from discrete code values in lookup tables to continuous phase configuration parameters calculated by the neural network. The auto-encoder model outputs optimized phase values that can take any value within the valid range, fully exploiting the continuous nature of phase shifter adjustment capabilities and improving beamforming performance
3Reliability
If pre-calibrated pointing angle-code tables are used, then the implementation is straightforward, but signal transmission accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by training the auto-encoder model offline before actual operation. During the training phase, the model learns the optimal mapping between pointing angles and phase configurations using simulated or measured data. Once trained, the model can directly compute accurate phase configurations during real-time operation without requiring complex online optimization, thus improving signal transmission accuracy while keeping runtime complexity manageable
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 improves signal transmission accuracy and efficiency by directly calculating phase configuration parameters, optimizing phase shifter performance and reducing storage requirements.
Implementation Method 1
the liquid crystal molecules rotate under the action of an electric field force, so as to change the dielectric constant, and thus change a transmission speed of an electromagnetic wave
Implementation Method 2
A liquid crystal phased array antenna uses the phase shifter which is formed based on a characteristic of an adjustable dielectric constant of a liquid crystal
Data Source
AI summary
The present disclosure provides a tunable antenna control method and apparatus, and a tunable antenna system. The tunable antenna control method includes: acquiring a beam pointing angle of a tunable antenna, calculating a phase-configuration parameter according to the beam pointing angle through a parameter calculation model, where the parameter calculation model is an artificial intelligence model taking the beam pointing angle as an input and the phase-configuration parameter of a phase shifter as an output, and controlling the phase shifter of the tunable antenna to perform phase configuration according to the phase-configuration parameter outputted by the parameter calculation model.