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DC Field | Value | Language |
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dc.contributor.author | Bai, C | - |
dc.contributor.author | Zhu, A | - |
dc.contributor.author | Lu, X | - |
dc.contributor.author | Zhu, Y | - |
dc.contributor.author | Wang, K | - |
dc.date.accessioned | 2023-06-20T18:14:27Z | - |
dc.date.available | 2023-06-20T18:14:27Z | - |
dc.date.issued | 2023-03-28 | - |
dc.identifier | ORCID iD: Chenyao Bai https://orcid.org/0000-0003-3510-390X | - |
dc.identifier | ORCID iD: Aoji Zhu https://orcid.org/0000-0002-1281-8892 | - |
dc.identifier | ORCID iD: Xiwen Lu https://orcid.org/0000-0002-3126-8996 | - |
dc.identifier | ORCID iD: Yunlong Zhu https://orcid.org/0000-0002-9645-6357 | - |
dc.identifier | ORCID iD: Kezhi Wang https://orcid.org/0000-0001-8602-0800 | - |
dc.identifier.citation | Bai, C. et al. (2023) 'Temporal Convolutional Network Based Signal Detection for Magnetotactic Bacteria Communication System', IEEE Transactions on NanoBioscience, 22 (4), pp. 943 - 955. doi: 10.1109/TNB.2023.3262555. | en_US |
dc.identifier.issn | 1536-1241 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26702 | - |
dc.description.abstract | Molecular communication (MC) aims to use signaling molecules as information carriers to achieve communication between biological entities. However, MC systems severely suffer from inter symbol interference (ISI) and external noise, making it virtually difficult to obtain accurate mathematical models. Specifically, the mathematically intractable channel state information (CSI) of MC motivates the deep learning (DL) based signal detection methods. In this paper, a modified temporal convolutional network (TCN) is proposed for signal detection for a special MC communication system which uses magnetotactic bacteria (MTB) as information carriers. Results show that the TCN-based detector demonstrates the best overall performance. In particular, it achieves better bit error rate (BER) performance than sub-optimal maximum a posteriori (MAP) and deep neural network (DNN) based detectors. However, it behaves similarly to the bidirectional long short term memory (BiLSTM) based detector that has been previously proposed and performs worse than the optimal MAP detector. When both BER performance and computational complexity are taken into account, the proposed TCN-based detector outperforms BiLSTM-based detectors. Furthermore, in terms of robustness evaluation, the proposed TCN-based detector outperforms all other DL-based detectors. | - |
dc.description.sponsorship | 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021M690701). | en_US |
dc.format.extent | 943 - 955 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | deep learning | en_US |
dc.subject | magnetotactic bacteria | en_US |
dc.subject | molecular communication | en_US |
dc.subject | quorum sensing | en_US |
dc.subject | signal detection | en_US |
dc.subject | temporal convolutional network | en_US |
dc.title | Temporal Convolutional Network Based Signal Detection for Magnetotactic Bacteria Communication System | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TNB.2023.3262555 | - |
dc.relation.isPartOf | IEEE Transactions on NanoBioscience | - |
pubs.issue | 4 | - |
pubs.publication-status | Published online | - |
pubs.volume | 22 | - |
dc.identifier.eissn | 1558-2639 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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