Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2023-07-20 DOI:10.1109/TMBMC.2023.3297135

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

引用次数: 1

Abstract

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.

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分子通信信道中符号检测的神经网络可解释性

最近的分子通信（MC）研究提出了用于符号检测的机器学习（ML）模型，避免了端到端信道模型的不可行性。然而，ML模型被应用为黑匣子，缺乏底层神经网络（NN）检测传入符号的正确性证明。本文研究了用于MC信道中符号检测的神经网络的可解释性方法。基于MC信道模型和实际测试台测量，我们生成合成数据并训练NN模型来检测MC信道中的二进制传输。使用局部可解释模型不可知解释（LIME）方法和个体条件期望（ICE），本文的研究结果证明了训练的神经网络与标准峰值和斜率检测器之间的相似性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Mathematics-Modeling and Simulation

CiteScore

3.90

自引率

13.60%

发文量

期刊介绍： As a result of recent advances in MEMS/NEMS and systems biology, as well as the emergence of synthetic bacteria and lab/process-on-a-chip techniques, it is now possible to design chemical “circuits”, custom organisms, micro/nanoscale swarms of devices, and a host of other new systems. This success opens up a new frontier for interdisciplinary communications techniques using chemistry, biology, and other principles that have not been considered in the communications literature. The IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (T-MBMSC) is devoted to the principles, design, and analysis of communication systems that use physics beyond classical electromagnetism. This includes molecular, quantum, and other physical, chemical and biological techniques; as well as new communication techniques at small scales or across multiple scales (e.g., nano to micro to macro; note that strictly nanoscale systems, 1-100 nm, are outside the scope of this journal). Original research articles on one or more of the following topics are within scope: mathematical modeling, information/communication and network theoretic analysis, standardization and industrial applications, and analytical or experimental studies on communication processes or networks in biology. Contributions on related topics may also be considered for publication. Contributions from researchers outside the IEEE’s typical audience are encouraged.