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Energy-Aware Power Allocation in Cooperative Communication Systems with Imperfect CSI

2013· article· en· W2020661221 on OpenAlexaff
Rajiv Devarajan, A. Punchihewa, Vijay K. Bhargava

Bibliographic record

VenueIEEE Transactions on Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransmitter power outputComputer scienceRelayPower budgetChannel state informationQuality of serviceComputer networkCommunications systemPower (physics)Transmission (telecommunications)Signal-to-noise ratio (imaging)Efficient energy useEnergy (signal processing)WirelessImperfectElectronic engineeringChannel (broadcasting)TelecommunicationsPower controlEngineeringElectrical engineeringTransmitterMathematics

Abstract

fetched live from OpenAlex

Energy-aware power allocation in cooperative communication systems depends on the availability of channel state information (CSI), which is often imperfect in practice. This paper proposes energy-aware, optimal power allocation schemes for an amplify-and-forward cooperative communication system in the presence of imperfect CSI, to i) minimize the total transmit power; ii) maximize the end-to-end signal-to-noise ratio (SNR). Energy awareness is incorporated in the schemes either by avoiding over-provisioning of quality-of-service (QoS) or, by preventing unsuccessful transmissions, depending on the application. In both the schemes, the source and relay nodes share a fixed total transmit power budget, and transmission is allowed only if the minimum required end-to-end SNR can be achieved with the available power budget. Closed-form expressions for the source and relay optimal transmit powers are derived for both these schemes. Performance of the systems under the proposed schemes are investigated through simulations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.258
Teacher spread0.232 · 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.

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

Citations16
Published2013
Admission routes1
Has abstractyes

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