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Record W2116958798 · doi:10.1109/glocom.2006.127

CTH16-5: On Selecting the Number of Receiver Diversity Antennas in Ricean Fading Cochannel Interference

2006· article· en· W2116958798 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

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFadingInterference (communication)Maximal-ratio combiningSignal-to-interference ratioAntenna (radio)Bit error rateComputer scienceElectronic engineeringAntenna diversityDiversity combiningDiversity gainSignal-to-noise ratio (imaging)Power (physics)TelecommunicationsMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The question of how many receiver antennas to employ in a diversity system operating in cochannel interference is examined. The long term signal-power-to-interference-power ratio, the long term signal amplitude to the square root of the interference power ratio, the average instantaneous signal-to- interference ratio, and the average bit error rate of a maximal ratio combining diversity system in the presence of an arbitrary number of cochannel interferers are evaluated when the desired user signal and the interfering user signals experience correlated Ricean fading. The best number of antennas required for a fixed-size antenna array to achieve a good compromise between the system performance and system cost is investigated. The results show that, in general, the performance of a fixed-size antenna array containing the maximum number of independent antennas can not be significantly improved by adding more than one additional antenna. Some special cases where particular gains can be achieved by adding additional correlated antennas are also discusssed.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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