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Thermodynamic Energy Cost and Bit Error Rate of Imperfect Transmitters in Molecular Communication

2023· article· en· W4392158480 on OpenAlexaff
Dongliang Jing, Lin Lin, Andrew W. Eckford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsYork University
FundersChina Postdoctoral Science Foundation
KeywordsImperfectBit error rateBit (key)Computer scienceEnergy costEnergy (signal processing)Error detection and correctionElectronic engineeringTelecommunicationsAlgorithmComputer networkEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

In some molecular communication (MC) designs, signaling molecules are held in reservoirs of different concentrations. This paper explores two thermodynamic implications of creating such a transmitter. First, it requires energy to generate reservoirs at different concentrations, since their chemical potential is different from the environment. Second, it requires an enormous energy cost to create a pure (or nearly pure) reservoir, so the transmitter is necessarily imperfect. Drawing from the Maxwell's Demon thought experiment, we consider the separation of an environmental mixture into reservoirs of differing concentrations, which requires free energy, but which allows information to be encoded in the difference of concentration. Both theoretical and simulation results indicate that the performance of the transmitter is positively correlated with the amount of consumed free energy. Furthermore, our simulation results indicate that there may be a fundamental thermodynamic tradeoff between energy per bit and bit error rate in MC.

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)0.002
metaresearch head score (Gemma)0.018
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.217
Teacher spread0.208 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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