Parameter Estimation for a Capacitive Coupling Communication Channel Within a Metal Cabinet Based on a Modified Artificial Immune Algorithm

作     者：Lu, Liping Guo, Zhe Wang, Zhiqi Huang, Zhonghua Liu, Ming 

作者机构：Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：75683-75698页

核心收录：

基　　金：National Natural Science Foundation of China 

主　　题：Integrated circuit modeling Couplings Metals Mathematical model Equivalent circuits Data models Computational modeling Channel model optimization parameter estimation artificial immune algorithm capacitive coupling sensor networks 

摘      要：The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.