RIS Reflection Coefficient Tuning for Low-Loss Signal Conversion
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Solution Overview
Problem
Current signal conversion technologies using reconfigurable intelligent surfaces (RIS) face challenges in optimizing reflection coefficients to minimize conversion loss and maximize spurious-free dynamic range (SFDR), particularly in scenarios requiring Doppler frequency shift simulation for applications like radar detection and wireless communication.
Innovation Solution
A method and device for selecting an optimized reflection coefficient for a reconfigurable intelligent surface (RIS) by generating a random entity population, selecting the coefficient with the highest similarity to a target signal frequency, and adjusting it based on a threshold value, using a genetic algorithm to iteratively refine the selection, incorporating reflection magnitude and phase coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional signal conversion methods are used with fixed reflection coefficients, then the system structure is simple, but conversion loss is high and spurious-free dynamic range is limited
Solution Approach 1:
The patent applies dynamics by transitioning from fixed reflection coefficients to dynamically adjustable reflection coefficients. The RIS reflection coefficients are optimized in real-time based on input signal characteristics, allowing the system to adapt to varying signal conditions and minimize conversion loss dynamically rather than relying on static pre-configured values.
Solution Approach 2:
The patent implements parameter changes by optimizing the reflection coefficient parameters (magnitude and phase) of the RIS based on the input signal's frequency and other characteristics. The system adjusts these parameters iteratively using a genetic algorithm to achieve optimal signal conversion performance, directly addressing the conversion loss issue through parameter optimization.
2Reliability
If fixed reflection coefficients are used in RIS, then the system is simple to implement, but spurious-free dynamic range is reduced
Solution Approach 1:
The patent applies feedback by implementing an iterative optimization process where the reflection coefficients are continuously adjusted based on the relationship between input signals and target signals. The genetic algorithm evaluates the performance metric (spurious-free dynamic range) and uses this feedback to guide the selection and refinement of reflection coefficient candidates, progressively improving system reliability.
Solution Approach 2:
The system implements self-service through the autonomous genetic algorithm that automatically optimizes reflection coefficients without requiring manual intervention. The algorithm independently evaluates candidate solutions, performs selection, crossover, and mutation operations, and converges to optimal parameters that maximize spurious-free dynamic range.
3Productivity
If reflection coefficients are not optimized, then the system operates quickly with simple processing, but frequency conversion performance is poor
Solution Approach 1:
The patent applies preliminary action by pre-defining the search space and evaluation criteria for reflection coefficient optimization before the actual optimization process begins. The system prepares the genetic algorithm parameters, establishes the performance metric framework, and organizes the RIS configuration options in advance, enabling efficient optimization execution when signal conversion is required.
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 minimizes conversion loss and maximizes SFDR by accurately matching the modulation signal to the target signal, enhancing frequency conversion performance and enabling effective Doppler frequency shift simulation for various industrial applications.
Implementation Method 1
reconfigurable intelligent surface (RIS) technology, which aims to tune a signal at a specific location by controlling a phase shift of the signal
Implementation Method 2
a planar surface composed of a large number of sub-wavelength elements capable of reflecting, scattering, and manipulating an electromagnetic wave
Data Source
AI summary
A signal conversion method and device for generating a modulation signal are provided. The signal conversion method includes receiving an input signal and a reflection coefficient of a reconfigurable intelligent surface (RIS) according to a sample time, selecting an optimized reflection coefficient of the RIS corresponding to the sample time, and generating the modulation signal by applying the optimized reflection coefficient of the RIS to the input signal.