As the promise of molecular communication via diffusion systems at nano-scale communication increases, designing molecular schemes robust to the inevitable effects of molecular interference has become of vital importance. We propose a novel approach of a CNN-based neural network architecture 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 a naive-approach index modulation scheme and symbol-by-symbol maximum likelihood estimation with respect to bit error rate, and demonstrate that the proposed method yields better performance.
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