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Record W4214889790 · doi:10.1109/taes.2022.3156087

When to Crossover From Earth to Space for Lower Latency Data Communications?

2022· article· en· W4214889790 on OpenAlexafffundabout
Aizaz U. Chaudhry, Halim Yanıkömeroğlu

Bibliographic record

VenueIEEE Transactions on Aerospace and Electronic Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsCarleton University
FundersNational Research Council Canada
KeywordsCrossoverLatency (audio)Computer scienceTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

For data communications over long distances, optical wireless satellite networks (OWSNs) can offer lower latency than optical fiber terrestrial networks (OFTNs). However, when is it beneficial to switch or crossover from an OFTN to an OWSN for lower latency data communications? In this article, we introduce a crossover function that enables to find the crossover distance, i.e., a distance between two points on the surface of the Earth beyond which switching or crossing over from an OFTN to an OWSN for data communications between these points is useful in terms of latency. Numerical results reveal that a higher refractive index of optical fiber (or $i$) in an OFTN and a lower altitude of satellites (or $h$) in an OWSN result in a shorter crossover distance. To account for the variation in the end-to-end propagation distance that occurs over the OWSN, we examine the crossover function in four different scenarios. Numerical results indicate that the crossover distance varies with the end-to-end propagation distance over an OWSN and is different for different scenarios. We calculate the average crossover distance over all scenarios for different $h$ and $i$ and use it to evaluate the simulation results. Furthermore, for a comparative analysis of OFTNs and OWSNs in terms of latency, we study three different OFTNs having different refractive indexes and three different OWSNs having different satellite altitudes in three different scenarios for long-distance intercontinental data communications, including connections between New York and Dublin, Sao Paulo and London, and Toronto and Sydney. All three OWSNs offer better latency than OFTN2 (with $i_{2}$ = 1.3) and OFTN3 (with $i_{3}$ = 1.4675) in all scenarios. For example, for Toronto–Sydney connection, OWSN1 (with $h_{1}$ = 300 km), OWSN2 (with $h_{2}$ = 550 km), and OWSN3 (with $h_{3}$ = 1100 km) perform better than OFTN2 by 18.11%, 16.08%, and 10.30%, respectively, while they provide an improvement in latency of 27.46%, 25.67%, and 20.54%, respectively, compared to OFTN3. OWSN1 performs better than OFTN1 (with $i_{1}$ = 1.1) for Sao Paulo–London and Toronto–Sydney connections by 2.23% and 3.22%, respectively, while OWSN2 outperforms OFTN1 for Toronto–Sydney connection by 0.82%. For New York–Dublin connection, all OWSNs while for Sao Paulo–London connection, OWSN2 and OWSN3 exhibit higher latency than OFTN1 as the corresponding average crossover distances are greater than the shortest terrestrial distances between cities in these scenarios. Multiple satellites (or laser intersatellite links) on its shortest paths drive up the propagation distance to the extent that OWSN3 ends up with a higher latency than OFTN1 for the Toronto–Sydney intercontinental connection scenario although the related average crossover distance is less than the shortest terrestrial distance between Toronto and Sydney. The challenges related to OWSNs and OFTNs that may arise from this work in future are also highlighted.

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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.269
Teacher spread0.227 · 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 designNot applicable
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

Citations31
Published2022
Admission routes3
Has abstractyes

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