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Record W2109032089 · doi:10.1109/ciss.2008.4558515

An efficient adaptive distributed space time coding scheme for cooperative relaying

2008· article· en· W2109032089 on OpenAlexaff
Jamshid Abouei, Hossein Bagheri, Amir K. Khandani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRelayComputer scienceRelay channelWirelessSignal-to-noise ratio (imaging)Coding (social sciences)Outage probabilityMonte Carlo methodPower (physics)Electronic engineeringChannel (broadcasting)Control theory (sociology)Real-time computingTopology (electrical circuits)Computer networkTelecommunicationsFadingMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

A non-regenerative dual-hop wireless system based on a distributed space-time-coding strategy is considered. It is assumed that each relay retransmits an appropriately scaled space-time coded version of received signals. The main goal of this paper is to investigate a power allocation strategy in relay stations using analytical and simulation arguments to satisfy the quality of service requirements. In the high signal-to-noise ratio regime for the relay-destination link, it is shown that the optimum power allocation strategy in each relay which minimizes the outage probability is to remain silent, if its channel gain with the source is less than a prespecified threshold level. The Monte-Carlo simulations show that the near-optimal power allocation scheme in each relay in order to minimize the outage probability or the frame-error rate is the threshold-based on-off power scheme. Also, the numerical results demonstrate a dramatic improvement in the system performance by using this scheme compared to the case that the relay stations forward their received signals with full power. Finally, a hybrid amplify-and-forward/detect-and-forward scheme is numerically evaluated.

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: Methods · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.742

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 studies0.0010.000
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 head0.053
GPT teacher head0.285
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations10
Published2008
Admission routes1
Has abstractyes

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