Abstract
Molecular communication is a novel approach for data transmission between miniaturised devices, especially in contexts where electrical signals are to be avoided. The communication is based on sending molecules (or other particles) at nanoscale through a typically fluid channel instead of the “classical” approach of sending electrons over a wire. Molecular communication devices have a large potential in future medical applications as they offer an alternative to antenna-based transmission systems that may not be applicable due to size, temperature, or radiation constraints. The communication is achieved by transforming a digital signal into concentrations of molecules that represent the signal. These molecules are then detected at the other end of the communication channel and transformed back into a digital signal. Accurately modeling the transmission channel is often not possible which may be due to a lack of data or time-varying parameters of the channel (e.g., the movements of a person wearing a medical device). This makes the process of demodulating the signal (i.e., signal classification) very difficult. Many approaches for demodulation have been discussed in the literature with one particular approach having tremendous success – artificial neural networks. These artificial networks imitate the decision process in the human brain and are capable of reliably classifying even rather noisy input data. Training such a network relies on a large set of training data. As molecular communication as a technology is still in its early development phase, this data is not always readily available. In this paper, we discuss neural network-based demodulation approaches relying on synthetic simulation data based on theoretical channel models as well as works that base their network on actual measurements produced by a prototype test bed. In this work, we give a general overview over the field molecular communication, discuss the challenges in the demodulations process of transmitted signals, and present approaches to these challenges that are based on artificial neural networks.
About the authors
Max Bartunik completed his master’s degree for electrical engineering in 2019 at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is currently pursuing his Ph.D. as a research assistant at the Institute for Electronics Engineering of the FAU. His main research topics are molecular communication and medical electronics.
Jens Kirchner studied physics at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and at University of St. Andrews, Scotland. He received his doctorates in 2008 and 2016 from FAU in the fields of biosignal analysis and philosophy of science to the Dr. rer. nat. and Dr. phil, respectively. Between 2008 and 2015 he worked at Biotronik SE & Co. KG in Erlangen and Berlin in the research and development of implantable cardiac sensors. Since 2015, he is with the Institute for Electronics Engineering at FAU, where he heads the Medical Electronics & Multiphysics Systems group. His research interests lie in wearable and implantable sensors, inductive power transfer, and molecular communication. He is a Senior Member of the IEEE with membership in the Communications Society, the Magnetics Society and the Engineering in Medicine and Biology Society.
Oliver Keszöcze is with the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany where he is a Junior Professor at the Chair for Hardware/Software-Co-Design since 2018. He received a Diploma degree in applied mathematics and a B.Sc. in Computer Science from the University of Bremen in 2011. After a short career as a Software Engineer, he decided to pursue a Ph.D. in Computer Science in 2012 at his Alma Mater. Since 2014 he was also a Researcher with the German Research Center for Artificial Intelligence. In 2017, he received his doctoral degree (Dr. rer. nat) at the University of Bremen. Prof. Keszocze’s research interests include several aspects of Logic Synthesis and Computer Aided Design in various application domains and for different technologies. His current research focuses on Approximate Computing for both, ASICs and FPGAs. He has been serving as a TPC member for several conferences, including DATE, ICCAD, and ASP-DAC and is a reviewer for IEEE TCAD as well as for several other journals. He is the organizer of the annual Special Session “Future Trends in Emerging Technologies” at the DSD Conference. He is glad to be able to support young researchers by being part of DATE conference’s PhD Forum TPC.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This work was supported in part by the German Federal Ministry of Education and Research (BMBF), project MAMOKO (16KIS0913K).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
[1] T. Nakano, T. Suda, M. Moore, R. Egashira, A. Enomoto, and K. Arima, “Molecular communication for nanomachines using intercellular calcium signaling,” in 5th IEEE Conference on Nanotechnology, 2005, vol. 2, pp. 478–481, 2005.Search in Google Scholar
[2] T. Nakano, A. W. Eckford, and T. Haraguchi, Molecular Communication, Cambridge, Cambridge University Press, 2013.10.1017/CBO9781139149693Search in Google Scholar
[3] T. Nakano and J.-Q. Liu, “Design and analysis of molecular relay channels: an information theoretic approach,” IEEE Trans. NanoBiosci., vol. 9, no. 3, pp. 213–221, 2010. https://doi.org/10.1109/tnb.2010.2050070.Search in Google Scholar
[4] M. Pierobon and I. F. Akyildiz, “Capacity of a diffusion-based molecular communication system with channel memory and molecular noise,” IEEE Trans. Inf. Theory, vol. 59, no. 2, pp. 942–954, 2013. https://doi.org/10.1109/tit.2012.2219496.Search in Google Scholar
[5] N. Farsad, W. Guo, and A. W. Eckford, “Tabletop molecular communication: text messages through chemical signals,” PLoS One, vol. 8, no. 12, p. e82935, 2013. https://doi.org/10.1371/journal.pone.0082935.Search in Google Scholar PubMed PubMed Central
[6] L. Grebenstein, J. Kirchner, R. S. Peixoto, et al.., “Biological optical-to-chemical signal conversion interface: a small-scale modulator for molecular communications,” IEEE Trans. NanoBiosci., vol. 18, no. 1, pp. 31–42, 2019. https://doi.org/10.1109/tnb.2018.2870910.Search in Google Scholar PubMed
[7] M. Damrath, S. Bhattacharjee, and P. A. Hoeher, “Investigation of multiple fluorescent dyes in macroscopic air-based molecular communication,” IEEE Trans. Mol. Biol. Multi-Scale Commun., vol. 7, no. 2, pp. 78–82, 2021. https://doi.org/10.1109/tmbmc.2021.3054877.Search in Google Scholar
[8] S. Lotter, L. Brand, V. Jamali, et al.., “Experimental research in synthetic molecular communications – Part I,” IEEE Nanotechnol. Mag., vol. 17, no. 3, pp. 42–53, 2023. https://doi.org/10.1109/mnano.2023.3262100.Search in Google Scholar
[9] S. Lotter, L. Brand, V. Jamali, et al.., “Experimental research in synthetic molecular communications – Part II,” IEEE Nanotechnol. Mag., vol. 17, no. 3, pp. 54–65, 2023. https://doi.org/10.1109/mnano.2023.3262377.Search in Google Scholar
[10] P. Lu, Z. Wu, and B. Liu, “A vertical channel model of molecular communication and its test-bed,” EAI Endorsed Trans. Pervasive Health Technol., vol. 3, no. 9, p. 152390, 2017. https://doi.org/10.4108/eai.21-3-2017.152390.Search in Google Scholar
[11] P. A. Hoeher, M. Damrath, S. Bhattacharjee, and M. Schurwanz, “On mutual information analysis of infectious disease transmission via particle propagation,” IEEE Trans. Mol. Biol. Multi-Scale Commun., vol. 8, no. 3, pp. 202–206, 2022. https://doi.org/10.1109/tmbmc.2021.3120637.Search in Google Scholar
[12] M. Kuscu, E. Dinc, B. A. Bilgin, H. Ramezani, and O. B. Akan, “Transmitter and receiver architectures for molecular communications: a survey on physical Design with modulation, coding, and detection techniques,” Proc. IEEE, vol. 107, no. 7, pp. 1302–1341, 2019. https://doi.org/10.1109/jproc.2019.2916081.Search in Google Scholar
[13] M. Bartunik, H. Unterweger, C. Alexiou, et al.., “Comparative evaluation of a new sensor for superparamagnetic iron oxide nanoparticles in a molecular communication setting,” in Bio-Inspired Information and Communication Technologies, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer International Publishing, 2020, pp. 303–316.10.1007/978-3-030-57115-3_27Search in Google Scholar
[14] M. Bartunik, T. Thalhofer, C. Wald, M. Richter, G. Fischer, and J. Kirchner, “Amplitude modulation in a molecular communication testbed with superparamagnetic iron oxide nanoparticles and a micropump,” in Body Area Networks. Smart IoT and Big Data for Intelligent Health, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer International Publishing, 2020, pp. 92–105.10.1007/978-3-030-64991-3_7Search in Google Scholar
[15] L. Deng, J. Li, J. T. Huang, et al., “Recent advances in deep learning for speech research at microsoft,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 8604–8608.10.1109/ICASSP.2013.6639345Search in Google Scholar
[16] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017. https://doi.org/10.1145/3065386.Search in Google Scholar
[17] V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient processing of deep neural networks: a tutorial and survey,” Proc. IEEE, vol. 105, no. 12, pp. 2295–2329, 2017. https://doi.org/10.1109/jproc.2017.2761740.Search in Google Scholar
[18] C. Lee, H. B. Yilmaz, C.-B. Chae, N. Farsad, and A. Goldsmith, “Machine learning based channel modeling for molecular MIMO communications,” in International Workshop on Signal Processing Advances in Wireless Comunications, 2017, pp. 1–5.10.1109/SPAWC.2017.8227765Search in Google Scholar
[19] H. B. Yilmaz, C. Lee, Y. J. Cho, and C.-B. Chae, “A machine learning approach to model the received signal in molecular communications,” in International Black Sea Conference on Communications and Networking, 2017, pp. 1–5.10.1109/BlackSeaCom.2017.8277667Search in Google Scholar
[20] L. C. Andrews, Special Functions of Mathematics for Engineers, 2nd ed USA, SPIE Press, 1998, p. 479.10.1117/3.270709Search in Google Scholar
[21] X. Qian and M. Di Renzo, “Receiver Design in molecular communications: an approach based on artificial neural networks,” in International Symposium on Wireless Communications, 2018, pp. 1–5.10.1109/ISWCS.2018.8491088Search in Google Scholar
[22] B.-H. Koo, H. J. Kim, J.-Y. Kwon, and C.-B. Chae, “Deep learning-based human implantable nano molecular communications,” in IEEE International Conference on Communications, 2020, pp. 1–7.10.1109/ICC40277.2020.9148818Search in Google Scholar
[23] M. Bartunik, O. Keszocze, B. Schiller, and J. Kirchner, “Deep learning to demodulate transmission in molecular communication,” in Workshop on Molecular Communications, 2022.Search in Google Scholar
[24] M. Bartunik, O. Keszocze, B. Schiller, and J. Kirchner, “Using deep learning to demodulate transmissions in molecular communication,” in International Symposium on Medical Information and Communication Technology, 2022.10.1109/ISMICT56646.2022.9828263Search in Google Scholar
[25] H. Unterweger, J. Kirchner, W. Wicke, et al.., “Experimental molecular communication testbed based on magnetic nanoparticles in duct flow,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018, pp. 1–5.10.1109/SPAWC.2018.8446011Search in Google Scholar
[26] D. P. Kingma and J. B. Adam, A Method for Stochastic Optimization. Comment: A Previous Version of This Article Has Been Published as a Conference Paper at the 3rd International Conference for Learning Representations, San Diego, 2015.Search in Google Scholar
[27] M. Bartunik, G. Fischer, and J. Kirchner, “The development of a biocompatible testbed for molecular communication with magnetic nanoparticles,” in IEEE Transactions on Molecular, Biological and Multi-Scale Commun, vol 35, no. 5, p. 1, 2023.Search in Google Scholar
[28] C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, 1948. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.Search in Google Scholar
[29] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. https://doi.org/10.1038/323533a0.Search in Google Scholar
[30] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735.Search in Google Scholar PubMed
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