Literature Database Entry


Roya Khanzadeh, Stefan Angerbauer, Jorge Torres Gómez, Pit Hofmann, Falko Dressler, Frank H. P. Fitzek, Andreas Springer and Werner Haselmayr, "Explainable Asymmetric Auto-Encoder for End-to-End Learning of IoBNT Communications," Proceedings of IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Stockholm, Sweden, May 2024.


The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional domains, such as the human body. A potential use case for IoBNT is the communication from the outside to the inside of the human body. In this scenario, typically the Receiver (RX) inside the human body has limited computational complexity, while the Transmitter (TX) outside has large computational resources. In this paper, we address this scenario and propose a novel Asymmetric Auto-Encoder (AAEC) architecture for end-to-end learning of a Molecular Communication (MC) system. It applies a Neural Network (NN) at the TX and a low-complexity slope detector at the RX. We discuss the different layers of the NN-based TX and the corresponding training approach. Moreover, we investigate the explainability of the NN-based TX and show through the use of meta modeling that it can be approximated by a linear model. In addition, we demonstrate that the proposed AAEC resembles an MC system with Zero Forcing (ZF) precoding for low and moderate Inter Symbol Interference (ISI). Finally, through numerical results, we confirmed the aforementioned findings and showed that the proposed AAEC outperforms MC systems with and without ZF precoding, especially in high ISI scenarios.

Quick access

Authors' Version PDF (PDF on this web site)
BibTeX BibTeX


Roya Khanzadeh
Stefan Angerbauer
Jorge Torres Gómez
Pit Hofmann
Falko Dressler
Frank H. P. Fitzek
Andreas Springer
Werner Haselmayr

BibTeX reference

    author = {Khanzadeh, Roya and Angerbauer, Stefan and Torres G{\'{o}}mez, Jorge and Hofmann, Pit and Dressler, Falko and Fitzek, Frank H. P. and Springer, Andreas and Haselmayr, Werner},
    title = {{Explainable Asymmetric Auto-Encoder for End-to-End Learning of IoBNT Communications}},
    publisher = {IEEE},
    address = {Stockholm, Sweden},
    booktitle = {IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024)},
    month = {5},
    year = {2024},

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at

This page was automatically generated using BibDB and bib2web.