Machine Learning Based Molecular Index Modulation

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.

Citation

https://arxiv.org/abs/2103.09812

@article{kara2021machine,
  title={Machine Learning Based Molecular Index Modulation},
  author={Kara, Ozgur and Yaylali, Gokberk and Pusane, Ali Emre and Tugcu, Tuna},
  journal={arXiv preprint arXiv:2103.09812},
  year={2021}
}

Acknowledgements

This work was supported in part by the Scientific and Technical Research Council of Turkey (TUBITAK) under Grant 119E190.