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Record W2753708791 · doi:10.1109/lpt.2017.2747147

Channel Estimation for Optical-OFDM-Based Multiuser MISO Visible Light Communication

2017· article· en· W2753708791 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

VenueIEEE Photonics Technology Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsOrthogonal frequency-division multiplexingPrecodingTransmitterChannel (broadcasting)Computer scienceVisible light communicationMultiplexingMinimum mean square errorBit error rateElectronic engineeringAlgorithmTelecommunicationsMIMOMathematicsStatisticsEngineeringOpticsEstimatorPhysics

Abstract

fetched live from OpenAlex

A least squares-based channel estimation algorithm is proposed for optical orthogonal frequency division multiplexing-based multiuser multiple input single output visible light communications. The pilot sequence and pilot tones are optimized to derive the minimum-mean-square error of the channel estimation. Each user estimates the channel independently, and sends the estimates of the channels to the transmitter. Based on the estimated channels, zero-forcing precoding is applied at the transmitter to achieve the spatial multiplexing gain. The simulated performances of channel estimation and data detection are provided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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