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Record W2152556510 · doi:10.1109/tcomm.2009.05.070599

Performance Analysis of the IEEE 802.15.4a UWB System

2009· article· en· W2152556510 on OpenAlexaff
Zahra Ahmadian, Lutz Lampe

Bibliographic record

VenueIEEE Transactions on Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConvolutional codeComputer sciencePulse-position modulationDecoding methodsBit error rateUltra-widebandPhase-shift keyingAlgorithmElectronic engineeringConcatenated error correction codeTime-hoppingCode rateCoding gainTelecommunicationsBlock codePulse-amplitude modulationDetectorEngineeringPulse (music)

Abstract

fetched live from OpenAlex

The recently approved IEEE 802.15.4a standard defines an ultra-wideband (UWB) based physical layer using concatenated coding with mixed binary phase-shift keying and binary pulse-position modulation (BPSK-BPPM) and direct-sequence spreading with time hopping. The concatenated code consists of an outer Reed-Solomon (RS) and an inner convolutional code, and the coding and modulation are combined such that both coherent and noncoherent receiver architectures are supported. In this paper, the error-rate performance of IEEE 802.15.4a compliant UWB radios is investigated. To this end, semi-analytical expressions for the bit-error rate (BER) and frame-error rate (FER) of the coded UWB system are derived. The presented framework is comprehensive in that (i) different methods for generating reliability information (i.e., decoding metrics), (ii) the effects of suboptimal multipath combining, and (iii) coherent and noncoherent reception methods are included. Furthermore, a particularly suited errors-and-erasures RS decoding scheme is devised. The evaluation of the error-rate expressions together with simulation results for realistic UWB channels show that (i) the error-rate approximations are tight over wide ranges of BER and FER, (ii) symbol-wise metrics are clearly advantageous over bit-wise metrics for decoding of the convolutional code, (iii) combining the 5 to 10 strongest multipath components approaches the performance of full combining within 1-2 dB for residential and 3-5 dB for outdoor UWB environments, and (iv) the simplicity of noncoherent detection comes at loss of more than 10 dB in signal-to-noise ratio compared to coherent detection.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.225
Teacher spread0.211 · 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

Citations44
Published2009
Admission routes1
Has abstractyes

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