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Record W1990842717 · doi:10.1002/ett.1231

Antenna combining for multi‐antenna multi‐relay channels

2007· article· en· W1990842717 on OpenAlex

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

VenueEuropean Transactions on Telecommunications · 2007
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
Fundersnot available
KeywordsRelayAntenna (radio)BeamformingComputer scienceMaximal-ratio combiningChannel (broadcasting)Antenna diversityConstraint (computer-aided design)Topology (electrical circuits)Power (physics)Electronic engineeringDiversity gainTelecommunicationsComputer networkElectrical engineeringMIMOMathematicsEngineeringFadingPhysics

Abstract

fetched live from OpenAlex

Abstract In this paper we analyse the performance of multiple relay channels when multiple antennas are deployed only at relays. We apply two antenna diversity techniques at relays, namely maximum ratio combining (MRC) on receive and transmit beamforming (TB). We show that for both decode‐and‐forward and amplify‐and‐forward relaying protocols, with K relays the network can be decomposed into K diversity channels each with a different channel gain, and that the signals can be effectively combined at the destination. We assume that the total number of antennas at all relays is fixed at N . With a reasonable power constraint at the relays, the network capacity will be lower bounded by that of N relay channels each with single antenna, and upper bounded by that of single relay channel with N antennas. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.092
GPT teacher head0.321
Teacher spread0.229 · 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