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Record W2919806614 · doi:10.1109/glocomw.2018.8644436

New MMSE Downlink Channel Estimation for Sub-6 GHz Non-Line-of-Sight Backhaul

2018· article· en· W2919806614 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
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMinimum mean square errorEstimatorBackhaul (telecommunications)Non-line-of-sight propagationPower delay profileComputer scienceTelecommunications linkAlgorithmMean squared errorChannel (broadcasting)WirelessElectronic engineeringMathematicsTelecommunicationsStatisticsDelay spreadFadingEngineering

Abstract

fetched live from OpenAlex

The minimum mean square error (MMSE) channel estimator employs the second-order statistics of the channel condition to minimize the mean square error. The major disadvantage of the MMSE estimator is its high complexity. In this paper, a new technique based on a one-dimensional MMSE method using frequency-domain interpolation followed by time-domain interpolation is proposed. Also, an energy efficient channel estimator based on power-delay-profile tracking and noise/interference power estimation method for sub-6 GHz backhaul link is analysed. The proposed algorithm is evaluated using a high delay spread Stanford University Interim SUI-5 and SUI-3 channel NLOS models for fixed backhaul wireless applications and small cells.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.352

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 head0.023
GPT teacher head0.257
Teacher spread0.235 · 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