Evidence map›Paper›PMID 35782190›Full record

ArticleIEEE internet of things journal2021

Resource Allocation for Multiuser Molecular Communication Systems Oriented to the Internet of Medical Things.

Xuan Chen, Miaowen Wen, Chan-Byoung Chae, Lie-Liang Yang, Fei Ji, Kostromitin Konstantin Igorevich

Abstract read
In one paragraph

Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xuan ChenSchool of Electronic and Information EngineeringSouth China University of Technology Guangzhou 510640 China.ORCID https://orcid.org/0000-0003-4964-6438
Miaowen WenSchool of Electronic and Information EngineeringSouth China University of Technology Guangzhou 510640 China.ORCID https://orcid.org/0000-0002-9638-7048
Chan-Byoung ChaeSchool of Integrated TechnologyYonsei University Seoul 03722 South Korea.ORCID https://orcid.org/0000-0001-9561-3341
Lie-Liang YangSchool of Electronics and Computer ScienceUniversity of Southampton Southampton SO17 1BJ U.K.ORCID https://orcid.org/0000-0002-2032-9327
Fei JiSchool of Electronic and Information EngineeringSouth China University of Technology Guangzhou 510640 China.ORCID https://orcid.org/0000-0003-1549-3322
Kostromitin Konstantin IgorevichDepartment of Information SecurityNRU South Ural State University 454000 Chelyabinsk Russia.ORCID https://orcid.org/0000-0003-2744-7489

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.

Indexed as

Combinatorial optimizationmolecular communication (MC)molecular resource allocationmultiple accesssum rateuser fairness

Identifiers

PMID35782190
PMCPMC8768980

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.