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Record W2565694439 · doi:10.1109/uemcon.2016.7777914

Optimal beamforming-based power control in wireless body area networks

2016· article· en· W2565694439 on OpenAlex
Hussein Moosavi, Francis M. Bui

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStackelberg competitionComputer scienceGoodputComputer networkBeamformingPower controlWirelessTransmitter power outputTelecommunications linkNash equilibriumGame theoryPower (physics)ThroughputTransmitterMathematical optimizationTelecommunicationsChannel (broadcasting)Mathematics

Abstract

fetched live from OpenAlex

We prove the existence and uniqueness of a Stack-elberg equilibrium when different selfish single antenna sensor nodes try to access a multiple antenna hub in the uplink of a wireless body area network (WBAN). The hub is the game leader, capable of punishing the deviating sensors by using its multiple antennas to block reception for a specified period of time. Sensor nodes are the followers, each seeking to choose a transmit power to independently maximize its utility, modeled as a weighted difference between its achievable goodput and the expense of transmission. At the same time, the hub seeks to force the nodes to communicate their packets at certain transmit powers that maximize the welfare of the WBAN. We demonstrate this setup enables the Stackelberg game to reach a unique subgame perfect equilibrium. The proposed game model is employed in an IEEE 802.15.6-based ultra wideband WBAN to numerically validate the merits of the framework.

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.

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)0.000
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: none
Teacher disagreement score0.716
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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