Grid-Based Antenna Pattern Optimization to Reduce Development Time
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Solution Overview
Problem
Current antenna design methods rely heavily on engineering experience and software simulations, leading to lengthy development times and potential failure to meet performance and environmental needs.
Innovation Solution
An antenna design method involving dividing the antenna zone into grids, randomly assigning conduction properties, and using an optimization algorithm to determine optimized conduction properties for each grid, thereby automating the design process to meet target specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If antenna design relies on engineers' experience and multiple software simulations, then design accuracy may be improved, but development time increases significantly
Solution Approach 1:
The system performs self-service by automatically generating antenna patterns through the optimization algorithm without requiring multiple manual simulation iterations. The algorithm autonomously searches for optimal antenna configurations that meet performance requirements, eliminating the need for engineers to repeatedly run simulations and manually adjust designs.
Solution Approach 2:
The optimization algorithm systematically varies antenna design parameters to explore the design space and identify optimal configurations. By automatically adjusting parameters such as antenna geometry, material properties, and structural characteristics, the system finds solutions that satisfy performance requirements while minimizing development time.
2Device complexity
If traditional antenna design methods are used, then design process may be simplified, but ability to meet performance and environmental needs deteriorates
Solution Approach 1:
The optimization algorithm incorporates feedback mechanisms by evaluating antenna performance against specified requirements and iteratively adjusting designs based on simulation results. The system continuously monitors whether design candidates meet performance and environmental needs, using this feedback to guide the search toward satisfactory solutions.
Solution Approach 2:
The design process transitions from static, experience-based methods to a dynamic, algorithm-driven approach. The optimization algorithm adaptively explores the design space, adjusting search strategies based on performance feedback, and can handle complex environmental constraints that would be difficult to address with traditional static design methods.
3Ease of operation
If engineers manually design antenna patterns, then design control is maintained, but design efficiency decreases
Solution Approach 1:
The system performs preliminary actions by automatically generating and evaluating multiple antenna design candidates before final selection. The optimization algorithm pre-explores the design space, identifies promising configurations, and presents optimized solutions that maintain design control while dramatically improving efficiency compared to manual iteration.
Data Source
AI summary
An antenna design method and an electronic device are provided. The method includes the following steps. Antenna specification information and antenna environment information are obtained, wherein the antenna specification information includes a target antenna parameter and an antenna size. An antenna zone corresponding to the antenna size is divided into multiple grids. A conduction property is randomly assigned to each of the grids to generate a random antenna pattern. Optimization algorithm is executed based on the target antenna parameter and the random antenna pattern to obtain an optimized conduction property for each of the grids. An antenna pattern designed to meet the antenna specification information is determined based on the optimized conduction property of each of the grids.