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A Novel LFM Waveform for Terahertz-Band Joint Radar and Communications over Inter-Satellite Links

2022· article· en· W4315605978 on OpenAlexaff
Gizem Sümen, Güneş Karabulut Kurt, Ali Görçin

Bibliographic record

VenueGLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsWaveformComputer scienceSatelliteCommunications satelliteRadarAmbiguity functionWirelessElectronic engineeringTerahertz radiationOrbit (dynamics)Communications systemBit error rateRemote sensingTelecommunicationsEngineeringPhysicsGeographyChannel (broadcasting)OpticsAerospace engineering

Abstract

fetched live from OpenAlex

There is no doubt that we need to keep our eyes on the sky as satellite networks aim to address the demands of 6G and beyond communications systems. On the other hand, the existence of millions of space debris pieces, large or small, poses a threat to the new space communications systems which consist of large number of small satellites, especially in the low-orbit. In this study, a dual-functioning pulsed linear frequency modulated (LFM) waveform at Terahertz (THz) bands is proposed for both wireless communications and space debris sensing over low-orbit inter-satellite links (ISLs). Initially, the ambiguity function of the proposed waveform is derived. Then, velocity and range estimation performance for the radar function and bit error rate performance for the communications function are investigated. Simulation results indicate significant performance gains in the THz-bands compared to the legacy LFM systems.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.277
Teacher spread0.224 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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Same venueGLOBECOM 2022 - 2022 IEEE Global Communications ConferenceSame topicRadar Systems and Signal ProcessingFrench-language works237,207