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

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.