  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.
