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Record W2119277930 · doi:10.1109/icc.2006.255491

Two-Stage Blind Detection of Alamouti Based Minimum-Shift Keying

2006· article· en· W2119277930 on OpenAlexaff
M.L.B. Riediger, P. Ho

Bibliographic record

Venue2006 IEEE International Conference on Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFadingPhase-shift keyingComputer scienceKeyingAlgorithmBit error rateChannel (broadcasting)Block codeChannel state informationMinimum-shift keyingTelecommunicationsElectronic engineeringMathematicsDecoding methodsEngineering

Abstract

fetched live from OpenAlex

In a recent study [7], the authors successfully incorporated minimum-shift keying (MSK) into an Alamouti-type space-time (ST) transmission system, operating in a fading channel environment. This work extends the investigation to include an enhanced receiver and a more realistic channel model that does not limit the fading to be constant over each ST block. The proposed receiver first detects an underlying differential ST π / 2 -shifted BPSK code present in the ST-MSK signal, by performing low-complexity non-coherent multiple-symbol differential detection with decision feedback. The relatively reliable initial data estimates are then used to obtain high-quality channel estimates, at a rate notably higher than the MSK symbol rate. The availability of these channel estimates enables a second-stage coherent sequence detector to significantly refine the initial data decisions. In fast fading, results show that the proposed receiver performance is within 2 dB of the coherent detection lower bound, at a bit-error-rate of 10-5. When compared to a linearly modulated ST π / 2 -shifted BPSK code, the proposed ST-MSK scheme achieves a greater order of diversity and hence better performance in a fast fading environment. We attribute this to the additional time-diversity effect associated with the phase coding evident in MSK.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.951

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 science0.0020.000
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 head0.070
GPT teacher head0.337
Teacher spread0.267 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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