Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels

IEEE Transactions on Molecular, Biological and Multi-Scale Communications(2023)

Cited 0|Views11
No score
Recent molecular communication (MC) research 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 NN model to detect 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.
Translated text
Key words
molecular communication channels,symbol detection,neural networks
AI Read Science
Must-Reading Tree
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined