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Record W1990324444 · doi:10.1109/aero.2008.4526425

A Novel Precoder Design for OFDM Receivers in Unknown Fading Channels

2008· article· en· W1990324444 on OpenAlex
Fumihiro Hasegawa, Konstantinos N. Plataniotis, Subbarayan Pasupathy

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

VenueProceedings - IEEE Aerospace Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingPairwise error probabilityEstimatorPrecodingFadingBit error rateChannel (broadcasting)Computer scienceMean squared errorMinimum mean square errorAlgorithmUpper and lower boundsMathematicsElectronic engineeringTelecommunicationsStatisticsEngineeringMIMO

Abstract

fetched live from OpenAlex

This paper presents a novel precoder design for an orthogonal frequency division multiplexing (OFDM) system using a channel estimator. First, an asymptotically tight approximation of the pairwise error probability (PEP) error with channel estimation error is presented and is shown to improve the existing upper bound of the PEP. Using the proposed approximation, a near-optimal power allocation scheme is derived and investigated and a new precoding scheme is introduced to improve the bit error rate (BER) performance of the receiver assisted by a minimum mean square error (MMSE) channel estimator. Both experimental and theoretical results included in this paper show improvement in the BER performance of a receiver with channel estimators utilizing the introduced precoder and power allocation scheme.

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: Methods · Consensus signal: none
Teacher disagreement score0.758
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.087
GPT teacher head0.269
Teacher spread0.182 · 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