A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Abstract

We report on the results of our investigations into more computationally efficient methods for propagation modeling in the implant environment through the use of neural networks. Our results are applicable to the important case of ultra-wideband systems operating in the implant environment. A propagation modeling engine based on neural networks can help the system designer avoid time-consuming numerical simulations normally utilized for propagation modeling. The contributions of this paper are as follows. We perform a systematic investigation of two neural network architectures for propagation modeling, namely the multilayer perceptron (MLP) and the recurrent Elman network. We perform a quantitative comparison of the results produced by these networks to the results produced by time-consuming numerical methods, such as the finite difference time domain (FDTD) method. Our results indicate that it is indeed possible to obtain path loss results using neural network methods that approach FDTD results within a 3- to 4-dB margin of error. We further illustrate that it is possible to attain this level of accuracy through the simpler MLP structure, and that the increased complexity and training time associated with the Elman network is unwarranted. We also observe that the MLP network can attain this performance through reasonable network sizes of 20–30 neurons.