Neural Network Detector in Mobile Molecular Communication for Fast Varying Channels

Abstract

In fast varying channels for Mobile molecular communication (MMC), detection is not easy. This challenge exists due to quick changes in the channel impulse response (CIR) in diffusive environment. The conventional detectors require channel state information (CSI) for accurate detection in MMC. Since, CSI is difficult to obtain in fast varying channels, the present research work proposes a neural network detector (NND) that does not require CSI in MMC, even for the channels varying rapidly. The NND uses BFGS algorithm for optimizing its weights. The performance of NND is determined using the data driven approach for training and testing. The bit error rate (BER) has been found for different numbers of nodes and layers. The optimized approach is carried out to trade off between computational burden and BER by variation of nodes as well as layers of the NND. In case of signal to noise ratio (SNR) of 39 dB, our network performs better than existing works in the literature.