Unsupervised Surrogate-Assisted Synthesis of Free-Form Planar Antenna Topologies for IoT Applications

2026-03-04

AI Summary

This work proposes an unsupervised, variable-fidelity framework for fully automated antenna synthesis tailored to Internet of Things applications, where designs must balance electrical performance, radiation characteristics, and environmental constraints. Addressing the limitations of manual design—susceptible to subjective bias—and existing automated approaches—hindered by inflexible geometric representations and high computational costs—the method employs a universal geometric representation to generate free-form planar antenna topologies. It integrates surrogate-assisted unsupervised classification to efficiently screen candidate structures and applies a two-stage gradient-based optimization for precise fine-tuning. By uniquely combining unsupervised topology generation with surrogate-assisted classification, the framework successfully synthesizes bandwidth-enhanced patch antennas in the 5–6 GHz and 6–7 GHz bands, significantly reducing computational overhead while enabling topology reuse, as demonstrated through extensive experimental validation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Design of antenna structures for Internet of Things (IoT) applications is a challenging problem. Contemporary radiators are often subject to a number of electric and/or radiation-related requirements, but also constraints imposed by specifics of IoT systems and/or intended operational environments. Conventional approaches to antenna design typically involve manual development of topology intertwined with its tuning. Although proved useful, the approach is prone to errors and engineering bias. Alternatively, geometries can be generated and optimized without supervision of the designer. The process can be controlled by suitable algorithms to determine and then adjust the antenna geometry according to the specifications. Unfortunately, automatic design of IoT radiators is associated with challenges such as determination of desirable geometries or high optimization cost. In this work, a variable-fidelity framework for performance-oriented development of free-form antennas represented using the generic simulation models is proposed. The method employs a surrogate-assisted classifier capable of identifying a suitable radiator topology from a set of automatically generated (and stored for potential re-use) candidate designs. The obtained geometry is then subject to a bi-stage tuning performed using a gradient-based optimization engine. The presented framework is demonstrated based on six numerical experiments concerning unsupervised development of bandwidth-enhanced patch antennas dedicated to work within 5 GHz to 6 GHz and 6 GHz to 7 GHz bands, respectively. Extensive benchmarks of the method, as well as the generated topologies are also performed.

Problem

• antenna design
• IoT applications
• unsupervised synthesis
• free-form topology
• optimization cost

Innovation

• surrogate-assisted optimization
• free-form antenna
• unsupervised design
• variable-fidelity modeling
• IoT antenna synthesis