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Record W2126110344 · doi:10.1109/tcomm.2005.861648

Effects of channel-estimation errors on receiver selection-combining schemes for Alamouti MIMO systems with BPSK

2006· article· en· W2126110344 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

VenueIEEE Transactions on Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhase-shift keyingRayleigh fadingSelection (genetic algorithm)Maximal-ratio combiningBit error rateAlgorithmChannel (broadcasting)Diversity combiningSignal-to-noise ratio (imaging)FadingMathematicsDiversity gainMIMOStatisticsComputer scienceTelecommunicationsElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

The bit-error rate (BER) of binary phase-shift keying in Rayleigh fading, using the Alamouti transmission scheme and receiver selection diversity in the presence of channel-estimation error, is studied. Closed-form expressions for the BER of log-likelihood ratio selection, signal-to-noise ratio (SNR) selection, switch-and-stay combining selection, and maximum ratio combining are derived in terms of the SNR and the cross-correlation coefficient of the channel gain and its corrupted estimate. Two new selection schemes, space-time sum-of-squares combining selection diversity and space-time sum-of-magnitudes selection diversity, are proposed and proven to provide almost the same performance as SNR selection, but with much simpler implementations. The effects of channel-estimation errors on each selection scheme are examined.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score1.000

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 head0.012
GPT teacher head0.246
Teacher spread0.233 · 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