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Record W2108488346 · doi:10.1109/vetecf.2008.72

A Frequency-Domain Correlation Matrix Estimation Algorithm for MIMO-OFDM Channel Estimation

2008· article· en· W2108488346 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsConcordia University
Fundersnot available
KeywordsFrequency domainTime domainAlgorithmOrthogonal frequency-division multiplexingComputer scienceMIMOChannel (broadcasting)MIMO-OFDMSIGNAL (programming language)Computational complexity theoryComputationTelecommunications

Abstract

fetched live from OpenAlex

The second-order statistics of a time-domain signal are very often used in blind and semi-blind channel estimation. Considering that the received signal in MIMO-OFDM systems might be corrupted in the time-domain due to some adverse factors such as frequency offset and large peak-to-average power ratio (PAPR), an IFFT processor is required in the receiver to achieve a good-quality time-domain signal. This additional IFFT incurs extra computational complexity and probably a long time delay as well in real-time communication systems. In this paper, we propose a new algorithm for the computation of the time-domain correlation matrix directly from the received frequency- domain signal. The proposed frequency-domain correlation matrix estimation method is then used to develop a new semi-blind MIMO-OFDM channel estimation approach. A number of computer simulation based experiments are conducted, confirming the effectiveness of the proposed method.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.162
Threshold uncertainty score0.806

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Quick stats

Citations6
Published2008
Admission routes1
Has abstractyes

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