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High-dimensional metric combining for non-coherent molecular signal detection
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Wei, Zhuangkun, Guo, Weisi, Li, Bin, Charmet, Jérôme and Zhao, Chenglin (2020) High-dimensional metric combining for non-coherent molecular signal detection. IEEE Transactions on Communications, 68 (3). pp. 1479-1493. doi:10.1109/TCOMM.2019.2959354 ISSN 0090-6778.
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WRAP-high-dimensional-metric-combining-non-coherent-molecular-signal-detection-Charmet-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1572Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TCOMM.2019.2959354
Abstract
In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbolinterference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different noncoherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as 10-4, in comparison with other state-of-the-art methods.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Internet of things , Nanonetworks, Neural computers , Gaussian processes , Machine learning, Signal detection | ||||||||
Journal or Publication Title: | IEEE Transactions on Communications | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 0090-6778 | ||||||||
Official Date: | March 2020 | ||||||||
Dates: |
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Volume: | 68 | ||||||||
Number: | 3 | ||||||||
Page Range: | pp. 1479-1493 | ||||||||
DOI: | 10.1109/TCOMM.2019.2959354 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2019 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. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 16 December 2019 | ||||||||
Date of first compliant Open Access: | 18 December 2019 |
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