MétaCan
Menu
Back to cohort
Record W2137916099 · doi:10.1109/icc.2009.5198781

Beamforming in Dual-Hop Fixed Gain Relaying Systems

2009· article· en· W2137916099 on OpenAlexaff
Daniel Benevides da Costa, Sonia Aı̈ssa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsHop (telecommunications)Moment-generating functionRelayBeamformingProbability density functionComputer scienceTopology (electrical circuits)Array gainOutage probabilitySignal-to-noise ratio (imaging)Antenna gainAntenna (radio)Electronic engineeringControl theory (sociology)Power (physics)TelecommunicationsMathematicsAntenna arrayElectrical engineeringEngineeringAntenna measurementPhysicsStatisticsAntenna factor

Abstract

fetched live from OpenAlex

The performance of beamforming in dual-hop cooperative networks with fixed-gain relays is investigated. These kinds of relays offer low complexity and ease of deployment when compared with variable-gain relays. In our analysis, the source and destination nodes are equipped with multiple antennas, whereas the relay is assumed to be a single-antenna device. Closed-form expressions for the outage probability (OP), probability density function (PDF), moment generating function (MGF), and generalized moments of the end-to-end signal-to- noise ratio (SNR) are obtained. It is shown that when the same antenna configurations are considered at the source and destination sides, the power imbalance between the hops may be either beneficial or detrimental for the overall system performance. In addition, depending on whether the average SNR of the second hop is equal, lower, or higher than that of the first hop, an increase of the number of antennas may not necessarily result in a substantial improvement in performance.

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)0.001
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: none
Teacher disagreement score0.982
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.039
GPT teacher head0.281
Teacher spread0.242 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicCooperative Communication and Network CodingFrench-language works237,207