Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels-Reference-Cited by-同舟云学术

Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels

Published:2023-09 Issue:3 Volume:9 Page:323-328
ISSN:2372-2061
Container-title:IEEE Transactions on Molecular, Biological and Multi-Scale Communications
language:
Short-container-title:IEEE Trans. Mol. Biol. Multi-Scale Commun.

Author:

Torres Gómez Jorge¹^ORCID,Hofmann Pit²^ORCID,Fitzek Frank H. P.³^ORCID,Dressler Falko¹^ORCID

Affiliation:

1. School for Electrical Engineering and Computer Science, TU Berlin, Berlin, Germany

2. Deutsche Telekom Chair of Communication Networks, Technische Universität Dresden, Dresden, Germany

3. Deutsche Telekom Chair of Communication Networks and the Centre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany

Funder

Project MAMOKO

German Federal Ministry of Education and Research

NaBoCom funded by the German Research Foundation

German Research Foundation (DFG) as part of Germany’s Excellence Strategy—EXC 2050/1—Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop” (CeTI) of Technische Universität Dresden

Federal Ministry of Education and Research (BMBF) in the Programme of “Souverän. Digital. Vernetzt.” Joint Project 6G-life

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Modeling and Simulation,Bioengineering,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6687308/10255331/10188873.pdf?arnumber=10188873

Reference23 articles.

1. Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

3. Testbed-based Receiver Optimization for SISO Molecular Communication Channels

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