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Record W1894608146 · doi:10.1109/dasc.2015.7311380

Spread spectrum design for aeronautical communication system with radio frequency interference

2015· article· en· W1894608146 on OpenAlexfundno aff
Gang Wang, Genshe Chen, Dan Shen, Xin Tian, Khanh Pham, Erik Blasch

Bibliographic record

Venue2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC) · 2015
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsDirect-sequence spread spectrumSpread spectrumFrequency-hopping spread spectrumRician fadingAvionicsCommunications systemFadingComputer networkComputer scienceJammingEngineeringTelecommunicationsElectronic engineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Communication link maintenance is crucial for aeronautical systems operations. To ensure the reliability, robustness, and security of aeronautical communication links; we investigate various types of interference in aeronautical communication systems, which can be categorized into unintentional and intentional interference. An interference model is built and incorporated into an aeronautical communication link design, where spread spectrum techniques are employed to mitigate the interference effects. To ensure comprehensive communication link quality-of-services, a direct-sequence spread spectrum (DSSS) and frequency-hopping spread spectrum (FHSS) are investigated and compared. Turbo coding is employed in conjunction with DSSS/FHSS for overall interference mitigation and performance evaluations. As a practical use case, the Rician fading channel is evaluated when analyzing the data link performances for three phases of air traffic surface management: taxing, takeoff/landing, and departure/approach scenarios. The results demonstrate consideration for future NextGen avionics designs for security and maintenance of communication links between the aircraft and air traffic control operations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.057
GPT teacher head0.251
Teacher spread0.194 · 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.

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

Citations9
Published2015
Admission routes1
Has abstractyes

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