Literature Database Entry

torres-gomez2023explainability


Jorge Torres Gómez, Pit Hofmann, Frank H. P. Fitzek and Falko Dressler, "Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels," IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 9 (3), pp. 323–328, September 2023.


Abstract

Recent research in molecular communication (MC) suggests machine learning (ML) models for symbol detection, avoiding the unfeasibility of end-to-end channel models. However, ML models are applied as black boxes, lacking proof of correctness of the underlying neural networks (NNs) to detect incoming symbols. This paper studies approaches to the explainability of NNs for symbol detection in MC channels. Based on MC channel models and real testbed measurements, we generate synthesized data and train a neural network (NN) model for the detection of binary transmissions in MC channels. Using the local interpretable model-agnostic explanation (LIME) method and the individual conditional expectation (ICE), the findings in this paper demonstrate the analogy between the trained NN and the standard peak and slope detectors.

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Jorge Torres Gómez
Pit Hofmann
Frank H. P. Fitzek
Falko Dressler

BibTeX reference

@article{torres-gomez2023explainability,
    author = {Torres G{\'{o}}mez, Jorge and Hofmann, Pit and Fitzek, Frank H. P. and Dressler, Falko},
    doi = {10.1109/TMBMC.2023.3297135},
    title = {{Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels}},
    pages = {323--328},
    journal = {IEEE Transactions on Molecular, Biological and Multi-Scale Communications},
    issn = {2332-7804},
    publisher = {IEEE},
    month = {9},
    number = {3},
    volume = {9},
    year = {2023},
   }
   
   

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Last modified: 2024-07-16