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Record W2122369397 · doi:10.1109/twc.2007.348316

Switching Rate and Dwell Time in M-of-N Selection Diversity

2007· article· en· W2122369397 on OpenAlexaff
J.K. Cavers, P. Ho

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsSimon Fraser University
FundersMedical Research Council
KeywordsDwell timeSelection (genetic algorithm)FadingDiversity combiningDiversity gainComputer scienceAlgorithmChannel (broadcasting)ReciprocalMathematicsDetectorDiversity schemeDoppler effectStatisticsChannel state informationTelecommunicationsPhysicsArtificial intelligenceWireless

Abstract

fetched live from OpenAlex

Many investigations of hybrid selection (HS) diversity assume maximal ratio combining (MRC) of the selected branches. However, a coherent detector needs to dwell on the received signal for some time before it can produce accurate channel estimates for fading compensation, a requirement that appears inconsistent with the branch switching that occurs in a selection diversity receiver. Motivated by this observation, we derive in this letter analytical results on the switching rate and average dwell time of a selection diversity receiver where M out of a total of N independent branches are selected for combining. We show that the switching rate can be many times the Doppler frequency, while the average dwell time can be a small fraction of the reciprocal Doppler frequency. The brevity of the dwell times suggests difficulty in obtaining channel state information, which in turn calls into question performance analyses of idealized HS/MRC structures. Our results also suggest that HS/MRC should be frame-based, rather than continuously acting in time

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.779

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 integrity0.0000.001
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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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 designBench or experimental
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

Citations26
Published2007
Admission routes1
Has abstractyes

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