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Record W4385757649 · doi:10.1109/lcomm.2023.3304324

Ergodic Capacity Analysis for Cooperative Ambient Communication System Under Sensitivity Constraint

2023· article· en· W4385757649 on OpenAlexaff
Wenjing Zhao, Jianchi Zhu, Xiaoming She, Gongpu Wang, Chintha Tellambura

Bibliographic record

VenueIEEE Communications Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBackscatter (email)Computer scienceSensitivity (control systems)Ergodic theoryWirelessTransmission (telecommunications)Electronic engineeringTopology (electrical circuits)TelecommunicationsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Cooperative ambient backscatter communication (CABC) is a promising and energy-efficient wireless technique, particularly relevant for green Internet of Things applications. Previous studies on CABC have often assumed that the tag can always perform backscatter operations. However, this assumption is not accurate since the tag needs sufficient power to support its circuit consumption for successful backscatter. In light of this, this article focuses on studying a CABC system with sensitivity constraints at the tag. The main contributions of this work are as follows. First, we derive the ergodic sum capacity of both primary transmission from the RF source to the reader and backscatter transmission from the tag to the reader. The resulting expressions are solved using Gaussian-Chebyshev quadrature (GCQ) to ensure accuracy and efficiency. Second, we present compact asymptotic results for low and high signal-to-noise ratios, enabling simplified calculations and analysis. Finally, we provide simulations to validate our analytical results. By considering sensitivity constraints at the tag, our study enhances the potential of CABC implementations in real-world scenarios.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.426
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.254
Teacher spread0.210 · 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 teacher head, 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

Citations11
Published2023
Admission routes1
Has abstractyes

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