{"title":"一种新的基于ML的分子通信符号检测流水线","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":"9 2","pages":"207-216"},"PeriodicalIF":2.4000,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":\"9 2\",\"pages\":\"207-216\"},\"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}","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}
A Novel ML-Based Symbol Detection Pipeline for Molecular Communication
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.
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.