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Record W2136956615 · doi:10.1109/wcnc.2011.5779313

On energy efficiency of cooperative communications in wireless body area network

2011· article· en· W2136956615 on OpenAlexaff
Xigang Huang, Hangguan Shan, Xuemin Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceRelayEnergy consumptionTransmission (telecommunications)Efficient energy usePath lossWirelessComputer networkChannel (broadcasting)Channel state informationTransceiverPower (physics)Constraint (computer-aided design)TelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, we investigate the energy efficiency of cooperative communications in wireless body area network (WBAN). We first analyze the outage performance of three transmission schemes, namely direct transmission, single-relay cooperation, and multi-relay cooperation. To minimize the energy consumption, we then study the problem of optimal power allocation with the constraint of targeted outage probability. Two strategies of power allocation are considered: power allocation with and without posture state information. Simulation results verify the accuracy of the analysis and demonstrate that: 1) power allocation making use of the posture information can reduce the energy consumption; 2) within a possible range of the channel quality in WBAN, cooperative communication is more energy efficient than direct transmission only when the path loss between the transmission pair is higher than a threshold; and 3) for most of the typical channel quality due to the fixed transceiver locations on human body, cooperative communication is effective in reducing energy consumption.

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)0.011
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.023
GPT teacher head0.222
Teacher spread0.199 · 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

Citations48
Published2011
Admission routes1
Has abstractyes

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