As the potential of molecular communication via diffusion (MCvD) systems at nano-scale communication increases, designing molecular schemes robust to the inevitable effects of molecular interference has become of vital importance. There are numerous molecular approaches in literature aiming to mitigate the effects of interference, namely inter-symbol interference. Moreover, for molecular multiple-input–multiple-output systems, interference among antennas, namely inter-link interference, becomes of significance. Inspired by the state-of-the-art performances of machine learning algorithms on making decisions, we propose a novel approach of a convolutional neural network (CNN)-based architecture. The proposed approach is for a uniquely-designed molecular multiple-input–single-output topology in order to alleviate the damaging effects of molecular interference. In this study, we compare the performance of the proposed network with that of an index modulation approach and a symbol-by-symbol maximum likelihood estimation and show that the proposed method yields better performance.

随着通过扩散（MCvD）系统在纳米级通信中的分子通信潜力的增加，设计对分子干扰不可避免的影响具有鲁棒性的分子方案变得至关重要。文献中有许多旨在减轻干扰影响的分子方法，即符号间干扰。此外，对于分子多输入多输出系统，天线之间的干扰，即链路间干扰变得很重要。受机器学习算法在决策方面最先进性能的启发，我们提出了一种基于卷积神经网络 (CNN) 架构的新方法。所提出的方法是针对独特设计的分子多输入-单输出拓扑结构，以减轻分子干扰的破坏性影响。在这项研究中，我们将所提出的网络的性能与索引调制方法和逐符号最大似然估计的性能进行了比较，并表明所提出的方法产生了更好的性能。