MétaCan
Menu
Back to cohort
Record W1999623030 · doi:10.1109/glocom.2006.606

SPCp1-04: Bit Mapping and Error Insertion for FEC Based PAPR Reduction in OFDM Signals

2006· article· en· W1999623030 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

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingBit error rateReduction (mathematics)Forward error correctionComputer scienceAlgorithmRedundancy (engineering)Additive white Gaussian noiseError detection and correctionDecoding methodsElectronic engineeringMathematicsTelecommunicationsWhite noiseChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

This paper proposes a new method for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems using forward error correction (FEC). The scheme combines the intentional insertion of correctable errors into the data stream with a specialized bit mapping. The latter deviates from traditional Gray encoding of bits into modulation symbols in order to maximize the benefits of FEC-based PAPR reduction. Using the code redundancy, the proposed scheme achieves significant reduction in PAPR and satisfactory bit error rate (BER) performance at the expense of acceptable computational complexity. Specifically, the complementary cumulative distribution function (CCDF) of the coded signal shows about a 5dB improvement over the original OFDM signal. In addition, BER performance in an additive white Gaussian noise demonstrates trade-offs between reduction in PAPR and remaining error correction capability of the deployed codes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.661

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 head0.017
GPT teacher head0.227
Teacher spread0.209 · 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