A Novel ML-Based Symbol Detection Pipeline for Molecular Communication

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2023-03-22 DOI:10.1109/TMBMC.2023.3278532

Valerio Selis;Daniel Tunç McGuiness;Alan Marshall

{"title":"A Novel ML-Based Symbol Detection Pipeline for Molecular Communication","authors":"Valerio Selis;Daniel Tunç McGuiness;Alan Marshall","doi":"10.1109/TMBMC.2023.3278532","DOIUrl":null,"url":null,"abstract":"Molecular Communication (MC) is the process of sending information by the use of particles instead of electromagnetic (EM) waves. This change in paradigm allows the use of MC in areas where EM transmission is undesirable. These include underground, underwater and even intra-body communications. While this novel paradigm promises new areas for communication, one of the major setbacks is its relatively low throughput caused by the propagation speed. This can be improved by decreasing the symbol duration; however, this can be a detriment to the correct decoding of symbols. This paper proposes a novel symbol detection pipeline to increase the possible throughput without increasing the error rate of the communication. This is based on a machine-learning algorithm for classification tasks using an L-point discrete time moving average filter and a wide range of features. Extensive simulations with long sequences at different signal-to-noise ratio (SNR) values were performed to determine how well the proposed method detects symbols. The results show that our method can detect symbols received when On-Off Keying (OOK) modulations are used with a 10 dB gain, even when transmissions with untrained SNR values occur.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10130469/","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: 0

Abstract

Molecular Communication (MC) is the process of sending information by the use of particles instead of electromagnetic (EM) waves. This change in paradigm allows the use of MC in areas where EM transmission is undesirable. These include underground, underwater and even intra-body communications. While this novel paradigm promises new areas for communication, one of the major setbacks is its relatively low throughput caused by the propagation speed. This can be improved by decreasing the symbol duration; however, this can be a detriment to the correct decoding of symbols. This paper proposes a novel symbol detection pipeline to increase the possible throughput without increasing the error rate of the communication. This is based on a machine-learning algorithm for classification tasks using an L-point discrete time moving average filter and a wide range of features. Extensive simulations with long sequences at different signal-to-noise ratio (SNR) values were performed to determine how well the proposed method detects symbols. The results show that our method can detect symbols received when On-Off Keying (OOK) modulations are used with a 10 dB gain, even when transmissions with untrained SNR values occur.

查看原文本刊更多论文

一种新的基于ML的分子通信符号检测流水线

分子通信（MC）是通过使用粒子而不是电磁波来发送信息的过程。这种模式的改变允许在EM传输不理想的地区使用MC。其中包括地下、水下甚至体内通信。虽然这种新的范式为通信提供了新的领域，但主要的挫折之一是传播速度导致的相对较低的吞吐量。这可以通过减少符号持续时间来改善；然而，这可能会损害符号的正确解码。本文提出了一种新的符号检测流水线，以在不增加通信错误率的情况下增加可能的吞吐量。这是基于一种用于分类任务的机器学习算法，该算法使用L点离散时间移动平均滤波器和广泛的特征。对不同信噪比（SNR）值的长序列进行了广泛的模拟，以确定所提出的方法检测符号的效果。结果表明，当使用增益为10dB的开-关键控（OOK）调制时，即使在发生具有未经训练的SNR值的传输时，我们的方法也可以检测接收到的符号。

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分钟内获得全文求助全文

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.