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Record W1998539767 · doi:10.1109/mape.2011.6156305

Performance analysis of DPP AM UWB systems over indoor fading channels

2011· article· en· W1998539767 on OpenAlexaff
Hao Zhang, Tingting Lu, Shanmei Zhang, T. Aaron Gulliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPulse-position modulationAdditive white Gaussian noisePulse-amplitude modulationFadingUltra-widebandComputer scienceModulation (music)Frame (networking)SIGNAL (programming language)Electronic engineeringTelecommunicationsChannel (broadcasting)Pulse (music)PhysicsEngineeringAcousticsDetector

Abstract

fetched live from OpenAlex

Differential pulse position amplitude modulation (DPPAM) is considered in Ultra Wideband (UWB) communic-ation systems in this paper. DPPAM combines differential pulse position modulation (DPPM) and pulse amplitude modulation (PAM) to provide good system performances and low com-putational complexity. A typical format for a DPPAM signal in UWB systems is derived from that of pulse position amplitude modulation (PPAM). The frame error rate (FER) of MTV-ary DPPAM systems over an additive white Gaussian noise (AWGN) channel and indoor fading channels are analyzed. The results show that the FER performance with 2×Nary DPPAM is better than that with 2×Nary DPPM and PPM, and MN-ary (M>;2) DPPAM provides a compromise between FER and complexity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.208
Teacher spread0.186 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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