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

机译：一种机器学习方法来模拟分子通信中接收信号的机器学习方法

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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.
机译：分子通信信道由接收信号确定，其形成专注于调制，接收器设计，容量和编码的研究的基础。因此，将接收分子的数量模拟直到时间T至关重要。当发射器是点并且接收器是吸收球体时，接收信号被分析地建模。利用第一击中过程（即，具有吸收接收器的漫射分子通信通道建模的基于扩散的分子通信通道是当变送器是反射球体时的开放问题。在本文中，我们利用人工神经网络技术来模拟用于球面发射器的接收信号和完美吸收接收器（即，第一击中过程）。所提出的技术可以用于假设球形发射器而不是点发射器的其他研究。

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