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Record W2029565597 · doi:10.1109/glocom.2014.7037271

Second-order statistic-based detection of Alamouti-coded OFDM signals for cognitive radio

2014· article· en· W2029565597 on OpenAlexaff
Yahia Ahmed, Octavia A. Dobre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingCognitive radioComputer scienceSynchronization (alternating current)AlgorithmModulation (music)Electronic engineeringStatisticSignal-to-noise ratio (imaging)SIGNAL (programming language)Detection theoryNoise (video)WiMAXChannel (broadcasting)WirelessTelecommunicationsMathematicsArtificial intelligenceEngineeringDetectorStatistics

Abstract

fetched live from OpenAlex

In this paper, an algorithm for the detection of the Alamouti-coded orthogonal frequency division multiplexing (AL-OFDM) signals is proposed. To the best of our knowledge, this is the first time in the literature when the detection of AL-OFDM signals used in recent WiMAX and LTE standards is investigated. The cross-correlation between the signals received with two antennas is studied as a detection feature, and its analytical closed-form expression obtained. These findings are further employed to develop the signal detection algorithm. The algorithm performance is investigated based on simulated standard signals. A good performance is achieved with a short sensing time and at low signal-to-noise ratios (SNRs). Additionally, the proposed algorithm requires neither information about the channel, modulation type, and noise power, nor timing synchronization.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.605

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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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 designOther design
Domainnot available
GenreMethods

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

Citations1
Published2014
Admission routes1
Has abstractyes

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