High-Precision Hybrid FA-PSO Based Inversion of Building Material Parameters for Fundamental Wireless Performance Evaluation

2026-07-14

AI Summary

This study addresses the challenge of accurately inverting the dielectric constant, electrical conductivity, and thickness of building materials. To this end, it proposes a hybrid optimization framework combining an adaptive firefly algorithm with particle swarm optimization (FA-PSO), enhanced by an optimized Gaussian-distribution-based initialization strategy to improve search efficiency. For the first time, the Cramér–Rao lower bound (CRLB) for electromagnetic parameter estimation is derived under complex Gaussian noise, establishing a theoretical performance benchmark for inversion accuracy. Experimental results demonstrate that, particularly for thin-layer materials, the proposed method achieves estimation accuracy approaching the CRLB and significantly outperforms existing approaches, thereby providing robust support for evaluating wireless propagation performance.

Abstract

In this paper, we propose an inversion method based on the firefly particle swarm optimization (FA-PSO) algorithm to estimate the permittivity, conductivity, and thickness of building materials using the free-space method. To improve convergence efficiency and robustness, an adaptive firefly algorithm (FA) is employed to systematically optimize the hyperparameters of the particle swarm optimization (PSO). By optimizing the parameters of the Gaussian distribution used for population initialization, the accuracy of parameter estimation is gradually improved. Furthermore, we derive the Cramer-Rao lower bound (CRLB) for the permittivity, conductivity, and thickness under a complex Gaussian noise model, which serves as a theoretical benchmark for evaluating the estimation accuracy of the FA-PSO algorithm. Numerical results indicate that for relatively thin materials, the estimation accuracy of the proposed method approaches this theoretical lower bound, confirming the effectiveness of the inversion framework. This study accurately extracts the electromagnetic properties of building materials, providing strong support for evaluating their wireless performance.

Problem

• parameter inversion
• building materials
• electromagnetic properties
• wireless performance evaluation
• Cramer-Rao lower bound

Innovation

• FA-PSO
• parameter inversion
• Cramer-Rao lower bound
• adaptive firefly algorithm
• free-space method