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
{"title":"Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels","authors":"Jorge Torres Gómez;Pit Hofmann;Frank H. P. Fitzek;Falko Dressler","doi":"10.1109/TMBMC.2023.3297135","DOIUrl":null,"url":null,"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.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10188873/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.90
自引率
13.60%
发文量
23
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.
期刊最新文献
Table of Contents IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Publication Information Guest Editorial Introduction to the Special Feature on the 8th Workshop on Molecular Communications Guest Editorial Special Feature on Seeing Through the Crowd: Molecular Communication in Crowded and Multi-Cellular Environments IEEE Communications Society Information
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1