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Record W2122571804 · doi:10.1109/icc.2005.1494904

Simplified Bit Error Rate Analysis of PAPM-UWB with MRC and EGC in Lognormal Fading Channels

2005· article· en· W2122571804 on OpenAlexaff
F. Rajwani, Norman C. Beaulieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaximal-ratio combiningLog-normal distributionFadingBit error rateAlgorithmDiversity combiningPulse-position modulationComputer scienceMathematicsModulation (music)Channel (broadcasting)Pulse-amplitude modulationElectronic engineeringPhysicsTelecommunicationsStatisticsPulse (music)EngineeringAcoustics

Abstract

fetched live from OpenAlex

Ultra-wideband (UWB) communications is a new technology being developed for high-speed, short distance wireless communications. One possible modulation scheme is pulse amplitude and position modulation (PAPM), which has a flat power spectral density that is highly suited to UWB. Closed-form expressions for the error rate performance of a PAPM UWB receiver based on maximal ratio combining (MRC) in a lognormal fading channel have been derived using Wilkinson's approximation for lognormal sums. The receiver is analyzed using a more accurate approximation to the lognormal sum distribution. Our approach is extended to obtain accurate closed-form expressions for the performance of equal gain combining (EGC). The performances of MRC and EGC diversity are calculated and compared to the results obtained using Wilkinson's approximation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2005
Admission routes1
Has abstractyes

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