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Record W2540600821 · doi:10.1109/icu.2005.1569955

Enhancing Multiband OFDM Performance: Capacity-Approaching Codes and Bit Loading

2006· article· en· W2540600821 on OpenAlexaff
Chris Snow, Lutz Lampe, Robert Schober

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingComputer scienceTurbo codeElectronic engineeringBit error ratePhysical layerChannel (broadcasting)Ultra-widebandTransmission (telecommunications)WirelessMultiplexingComputer networkTelecommunicationsDecoding methodsEngineering

Abstract

fetched live from OpenAlex

In this paper we consider turbo and repeat-accumulate (RA) codes as well as bit-loading as methods of enhancing the performance of multiband OFDM, a frequency-hopping orthogonal frequency-division multiplexing system which is a strong contender for the physical layer IEEE standard for high-rate wireless personal area networks (WPANs) based on ultra-wideband (UWB) transmission. Our methodology consists of (a) development and quantification of appropriate information-theoretic performance measures, and (b) comparison of these measures with simulation results for the multiband OFDM standard proposal as well as our proposed extensions. We find that the current multiband OFDM standard proposal sufficiently exploits the frequency selectivity of the UWB channel, and that the system performs in the vicinity of the channel cutoff rate. By applying turbo codes and a reduced-complexity clustered bit-loading algorithm the system power efficiency can be improved by over 6 dB at a data rate of 480 Mbps.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.482

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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

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