RIS Path-Loss Modeling for Metasurface Size and Placement
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Solution Overview
Problem
Existing path-loss modeling for reconfigurable intelligent surfaces (RIS) is complex and relies on intricate empirical parameters that are difficult to characterize, leading to inefficiencies in designing and implementing RIS placement and size.
Innovation Solution
A simplified path-loss model based on large distance approximation and aperture efficiency analysis, using geometric parameters and directivity of the feed antenna, reduces the need for complex characterization and simplifies the design process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional path-loss modeling methods are used for RIS, then measurement precision may be maintained, but device complexity increases significantly due to intricate empirical parameters
Solution Approach 1:
The patent transforms the path-loss model from using complex empirical parameters (amplitude reflection coefficients, phase shifts, unit-cell patterns) to using simple geometric parameters (RIS area, distance, angles). This parameter substitution maintains measurement precision while dramatically reducing model complexity, making the model practical for deployment.
Solution Approach 2:
The patent extracts and removes the complex empirical parameters from the path-loss model, keeping only the essential geometric parameters. By taking out the intricate characterization requirements (amplitude and phase measurements of individual unit cells), the model becomes much simpler while retaining its ability to accurately predict path loss.
2Measurement precision
If detailed empirical parameter characterization is performed, then measurement precision improves, but loss of time increases due to extensive measurement and characterization requirements
Solution Approach 1:
The patent removes the time-consuming empirical parameter characterization steps from the design process. By extracting only the necessary geometric parameters (which are trivial to obtain) and eliminating the need for detailed amplitude and phase measurements of unit cells, the model enables rapid RIS placement optimization without sacrificing path-loss prediction accuracy.
Solution Approach 2:
The patent performs preliminary simplification of the path-loss model by pre-deriving a closed-form expression that depends only on geometric parameters. This preliminary action eliminates the need for time-consuming measurements during the design and deployment phases, allowing practitioners to directly calculate path loss using basic geometric information.
3Measurement precision
If standard path-loss models with many variables are used, then measurement precision may be maintained, but ease of operation deteriorates due to difficulty in characterizing multiple parameters
Solution Approach 1:
The patent fundamentally changes the parameter set from complex electromagnetic characteristics (amplitude reflection coefficients, phase shifts, unit-cell radiation patterns) to simple geometric parameters (RIS area, distance from transmitter and receiver, incident and reflected angles). This transformation maintains path-loss estimation accuracy while making the model extremely easy to operate with readily available geometric information.
Solution Approach 2:
The patent focuses on the most critical local geometric parameters that dominate path-loss behavior (RIS area, distance, and angles) while ignoring less significant electromagnetic parameter variations. This local quality approach captures the essential physics with minimal parameters, greatly simplifying operation.
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
The model allows for more efficient and practical RIS size and placement optimization, achieving accurate gain calculations with minimal computational effort and accessible design variables, overcoming the limitations of traditional methods.
Implementation Method 1
These elements can be phase-shifting units or resonators that modify the phase, amplitude, and polarization of incident electromagnetic waves to redirect (e.g., reflect or refract) incoming electromagnetic beams in a fixed direction
Implementation Method 2
redirect (e.g., reflect or refract) incoming electromagnetic beams
Implementation Method 3
a path loss model for reconfigurable intelligent surface (RIS) placement and/or size, including: a receiver gain level corresponding to a receiver that receives a reflected signal from the metasurface
Data Source
AI summary
The technology described herein is directed towards designing and configuring a reconfigurable intelligent surface for deployment, based on a straightforward path-loss model having simplified input variables available to a designer, and having mitigated characterization complexity when compared to other path loss models. The relatively large distance that exists between the feed antenna and a reconfigurable intelligent surface facilitates approximation of certain factors, resulting in a practical solution for design and deployment of a reconfigurable intelligent surface of interest. The input variables include the geometry of the reconfigurable intelligent surface, receiver gain, transmitter gain, and the directivity of the transmitting antenna, which are parameters that are easily available to a designer for deploying a reconfigurable intelligent surface. A reconfigurable intelligent surface deployment position and/or size can be determined via an iterative optimization approach, to optimize the position and/or size based on a defined optimization cost expression.