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Record W2107910370 · doi:10.1109/ccece.1999.807175

A robust high speed indoor wireless communications system using chirp spread spectrum

2003· article· en· W2107910370 on OpenAlexaff
J.Q. Pinkney, A.B. Sesay, S. Nichols, Rayhan Behin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChirp spread spectrumComputer scienceChirpMultipath propagationPhase-shift keyingRake receiverElectronic engineeringDelay spreadTransmitterSpread spectrumWirelessTransmission (telecommunications)Bit error rateChannel (broadcasting)Direct-sequence spread spectrumTelecommunicationsPhysicsOpticsEngineering

Abstract

fetched live from OpenAlex

This paper considers the implications of using chirp spread spectrum (CSS) as an antimultipath modulation technique for the transmission of high speed (>10 Mb/s) data in the indoor wireless channel. Three elements are required for a CSS system to perform optimally in the multipath environment-a chirp spreader/correlator for resolution of the channel multipath, a phase-differential modulation scheme to co-phase the multipath components and a RAKE for recombination of the symbol energy. The advantage of CSS is that all three of these elements can be implemented with simple analog hardware. A prototype CSS system is presented along with simulated and measured performance plots in the indoor channel. Test results indicate that a CSS system using DQPSK can perform within 2 dB of Gaussian DQPSK in 10 m non-line of sight (NLOS) indoor channels with excess delay >200 ns, down to bit error rates of 10/sup -7/ with single omni antennas in transmitter and receiver and no coding, at data rates of 20 Mb/s and higher.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.001

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 head0.039
GPT teacher head0.252
Teacher spread0.213 · 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 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

Citations17
Published2003
Admission routes1
Has abstractyes

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