A machine learning approach to model the received signal in molecular communications

A molecular communication channel is determined by the received signal, which forms the basis for studies that are focusing on modulation, receiver design, capacity, and coding. Therefore, it is crucial to model the number of received molecules until time t. Received signal is modeled analytically when the transmitter is a point and the receiver is an absorbing sphere. Modeling the diffusion-based molecular communication channel with the first-hitting process (i.e., with an absorbing receiver) is an open issue when the transmitter is a reflecting spherical body. In this paper, we utilize the artificial neural networks technique to model the received signal for a spherical transmitter and a perfectly absorbing receiver (i.e., first-hitting process). The proposed technique may be utilized in other studies that assume a spherical transmitter instead of a point transmitter.