Neural Network based Distance Estimation for Branched Molecular Communication Systems
Schottlender M, Schäfer M, Veiga RA (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: Association for Computing Machinery, Inc
Pages Range: 28-33
Conference Proceedings Title: NanoCom 2025 - Proceedings of the 12th ACM International Conference on Nanoscale Computing and Communication
Event location: Chengdu, CHN
ISBN: 9798400721663
Abstract
Molecular Communications (MC) is an emerging research paradigm that utilizes molecules to transmit information, with promising applications in biomedicine such as targeted drug delivery or tumor detection. It is also envisioned as a key enabler of the Internet of BioNanoThings (IoBNT). In this paper, we propose algorithms based on Recurrent Neural Networks (RNN) for the estimation of communication channel parameters in MC systems. We focus on a simple branched topology, simulating the molecule movement with a macroscopic MC simulator. The Deep Learning architectures proposed for distance estimation demonstrate strong performance within these branched environments, highlighting their potential for future MC applications.
Authors with CRIS profile
Involved external institutions
Argentina (AR)How to cite
APA:
Schottlender, M., Schäfer, M., & Veiga, R.A. (2025). Neural Network based Distance Estimation for Branched Molecular Communication Systems. In NanoCom 2025 - Proceedings of the 12th ACM International Conference on Nanoscale Computing and Communication (pp. 28-33). Chengdu, CHN, CN: Association for Computing Machinery, Inc.
MLA:
Schottlender, Martin, Maximilian Schäfer, and Ricardo A. Veiga. "Neural Network based Distance Estimation for Branched Molecular Communication Systems." Proceedings of the 12th ACM International Conference on Nanoscale Computing and Communication, NanoCom 2025, Chengdu, CHN Association for Computing Machinery, Inc, 2025. 28-33.
BibTeX: Download