Scaled Conjugate Gradient Algorithm for Neural Network Detector in Mobile Molecular Communication

Abstract

In this work, a neural network (NN)-based detection for mobile molecular communication via diffusion (MCvD) is proposed. The proposed detector employs a scaled conjugate gradient (SCG) algorithm for updating the weights of the NN. Moreover, three different techniques are used in training and detection by the NN. These techniques correspond to i) filtered signal, ii) slope values of the filtered signal, and iii) concentration difference of the filtered signal in a bit interval. More specifically, a sequence of transmitted bit pattern and each of the above three techniques are used separately to train the NN. After training, the NN-based detector performs detection under a time-varying channel. The bit error rate (BER) performance of the proposed SCG algorithm for the NN-based detector is also compared with a first-order algorithm Gradient Descent (GD) and a second-order algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) for different coherence times of the channel. Simulation results demonstrate that the NN detector using SCG outperforms the BFGS if slope values are used for training the NN. Further, the SCG algorithm has a significant performance gain compared to the GD algorithm.