Demodulation with Deep Neural Networks for Time-Dependent Flow-Based Molecular Communication Channels
Kosanetzki D, Keszöcze O, Kroll L, Thiem J, Kirchner J (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025
Event location: Barcelona, ESP
ISBN: 9798331520427
DOI: 10.1109/ICMLCN64995.2025.11140309
Abstract
A major targeted application scenario of molecular communication (MC) is information transmission in biological systems and in the human body in particular. These systems, however, are strongly time-dependent due to their interaction with a changing environment, and so are the corresponding transmission channels. To address this issue for flow-based channels, a deep neural network consisting of a heterogeneous ensemble containing a convolutional neural network (CNN) block architecture and a bi-directional long short-term memory (LSTM) model is proposed to improve demodulation for a straight tube with time-varying length, a scenario oriented towards MC in the cardiovascular system. For evaluation, simulated data is generated with a receiver position oscillating in axial direction. The proposed neural network architecture achieves an accuracy of 97 % and a bit error rate (BER) of 0.0028, which demonstrates its capability of dealing with data transmission setups with varying channel parameters. If trained with data from a static receiver, however, accuracy and BER worsen to 10 % and 0.3, respectively, which demonstrates the need for demodulation techniques tailored for dynamically changing MC setups, as particularly present in biological systems.
Involved external institutions
Fachhochschule Dortmund - University of Applied Sciences
Germany (DE)
Technical University of Denmark / Danmarks Tekniske Universitet (DTU)
Denmark (DK)
Germany (DE)
Technical University of Denmark / Danmarks Tekniske Universitet (DTU)
Denmark (DK)
How to cite
APA:
Kosanetzki, D., Keszöcze, O., Kroll, L., Thiem, J., & Kirchner, J. (2025). Demodulation with Deep Neural Networks for Time-Dependent Flow-Based Molecular Communication Channels. In 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025. Barcelona, ESP: Institute of Electrical and Electronics Engineers Inc..
MLA:
Kosanetzki, Dorian, et al. "Demodulation with Deep Neural Networks for Time-Dependent Flow-Based Molecular Communication Channels." Proceedings of the 2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025, Barcelona, ESP Institute of Electrical and Electronics Engineers Inc., 2025.
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