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Record W2547895477 · doi:10.1109/lcomm.2016.2624727

Enabling Energy Efficient Molecular Communication via Molecule Energy Transfer

2016· article· en· W2547895477 on OpenAlexaff
Yansha Deng, Weisi Guo, Adam Noel, Arumugam Nallanathan, Maged Elkashlan

Bibliographic record

VenueIEEE Communications Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMolecular communicationEnergy (signal processing)Energy transferComputer scienceMoleculeChemistryComputer networkChemical physicsPhysicsChannel (broadcasting)Transmitter

Abstract

fetched live from OpenAlex

Molecular communication via diffusion (MCvD) is inherently an energy efficient transportation paradigm, which requires no external energy during molecule propagation. Inspired by the fact that the emitted molecules have a finite probability to reach the receiver, this letter introduces an energy efficient scheme for the information molecule synthesis process of MCvD via a simultaneous molecular information and energy transfer (SMIET) relay. With this SMIET capability, the relay can decode the received information as well as generate its emission molecules using its absorbed molecules via chemical reactions. To reveal the advantages of SMIET, approximate closed-form expressions for the bit error probability and the synthesis cost of this two-hop molecular communication system are derived and then validated by particle-based simulation. Interestingly, by comparing with a conventional relay system, the SMIET relay system can be shown to achieve a lower minimum bit error probability via molecule division, and a lower synthesis cost via molecule type conversion or molecule division.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.201
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2016
Admission routes1
Has abstractyes

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