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Error Probability Analysis of Time-Hopping Biorthogonal Pulse Position Modulation UWB Systems with a RAKE Receiver over Indoor Multi-Path Fading Channels

2012· article· en· W2023212174 on OpenAlexaff
Hao Zhang, Ting Ting Lv, T. Aaron Gulliver

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Victoria
FundersProgram for New Century Excellent Talents in UniversityMedical Research Council
KeywordsFadingRake receiverUltra-widebandPulse-position modulationBiorthogonal systemTime-hoppingElectronic engineeringComputer scienceRakeBit error rateInterference (communication)Channel (broadcasting)TelecommunicationsEngineeringPulse (music)Pulse-amplitude modulationArtificial intelligenceWavelet

Abstract

fetched live from OpenAlex

This paper presents the error probability analysis of Time-Hopping Biorthogonal Pulse Position Modulation (TH-BPPM) ultra-wideband (UWB) systems with a RAKE receiver over indoor multi-path fading channels. UWB signals suffer from severe multi-path interference when employed in an indoor fading environment. A RAKE receiver can be used to improve the performance of UWB systems. TH-BPPM has attracted much attention in recent years due to its many advantages, such as low probability of error and low complexity. In this paper, the IEEE 802.15.3a indoor channel model is employed to analyze the performance of TH-BPPM UWB systems with different RAKE receivers. The bit error rate (BER) of ARake, PRake, and SRake TH-BPPM UWB systems is derived. The results indicate that ARake has the best performance, SRake is better than PRake when the number of fingers is same.

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)0.001
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 score0.279
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.325
Teacher spread0.277 · 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".

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Citations0
Published2012
Admission routes1
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