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Record W2148142964 · doi:10.1109/iwcmc.2011.5982640

Performance of power allocation schemes in a two-hop AF relay system with faded direct link

2011· article· en· W2148142964 on OpenAlexaff
Hamed Rasouli, Alagan Anpalagan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRelayRayleigh fadingHop (telecommunications)WirelessTransmitter power outputComputer scienceTransceiverComputer networkPower (physics)Bit error rateEfficient energy useFadingElectronic engineeringTelecommunicationsElectrical engineeringEngineeringPhysicsChannel (broadcasting)Transmitter

Abstract

fetched live from OpenAlex

Though power allocation has been studied extensively in the literature, as energy efficiency becomes more important in “green” wireless communication, we revisit to study the differences in SNR vs BER-based power allocation (PA) for relay communication when the direct link is severely faded. The average SNR and average BER expressions are first derived for an amplify-and-forward relaying protocol with Rayleigh fading channels. Based on the derived expressions at the destination through two-hop communication, closed-form expressions for optimum transmit power are obtained at the source and the relay. It is observed that the higher the total transmit power is, the bigger portion of it should be allocated to the relay independent of its location. For a given end-to-end BER, optimally selected relay's location is relatively more closer to the source if BER-based PA is employed than that of the SNR-based PA. It is also noted that the BER-based power allocation scheme can achieve 1dB performance improvement over the SNR-based counterpart when relay is centrally located and is in the higher transmit power regime resulting in energy saving, for a two-hop wireless system with no avail of direct communication between transceivers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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