Molecular communication is a novel approach for data transmission between
miniaturized devices, especially in contexts where electrical signals are to be
avoided. The communication is based on sending molecules (or other particles)
at nano scale through channel instead sending electrons over a wire. Molecular
communication devices have a large potential in medical applications as they
offer an alternative to antenna-based transmission systems that may not be
applicable due to size, temperature, or radiation constraints. The
communication is achieved by transforming a digital signal into concentrations
of molecules. These molecules are then detected at the other end of the
communication channel and transformed back into a digital signal. Accurately
modeling the transmission channel is often not possible which may be due to a
lack of data or time-varying parameters of the channel (e. g., the movements of
a person wearing a medical device). This makes demodulation of the signal very
difficult. Many approaches for demodulation have been discussed with one
particular approach having tremendous success: artificial neural networks.
These networks imitate the decision process in the human brain and are capable
of reliably classifying noisy input data. Training such a network relies on a
large set of training data. As molecular communication as a technology is still
in its early development phase, this data is not always readily available. We
discuss neural network-based demodulation approaches relying on synthetic data
based on theoretical channel models as well as works using actual measurements
produced by a prototype test bed. In this work, we give a general overview over
the field molecular communication, discuss the challenges in the demodulations
process of transmitted signals, and present approaches to these challenges that
are based on artificial neural networks.

分子通信是一种在避免使用电信号的情况下，在微型设备之间进行数据传输的新方法。以纳米尺度通过信道发送分子（或其他粒子），而不是通过导线发送电子。我们讨论了基于人工神经网络的解调方法，这些网络模拟人脑的决策过程，并能可靠地对噪声干扰下的输入数据进行分类。