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Experimental Assessment of Misalignment Effects in Terahertz Communications

2024· article· en· W4402157345 on OpenAlexaff
Hasan Nayır, Erhan Karakoca, Güneş Karabulut Kurt, Ali Görçin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTerahertz radiationTerahertz metamaterialsComputer scienceOptoelectronicsMaterials sciencePhysicsOpticsFar-infrared laserLaser

Abstract

fetched live from OpenAlex

Terahertz (THz) frequencies play a crucial role in the advancement of next-generation wireless systems, primarily owing to their substantial available bandwidths. The inherent limitation of limited range, attributed to high attenuation in these frequencies, can be effectively addressed by implementing densely deployed heterogeneous networks, complemented by Unmanned Aerial Vehicles (UAVs) within a three-dimensional hyperspace. Yet, the success of THz communications relies on the precise alignment of beams. Inadequate handling of beam alignment can lead to diminished signal strength at the receiver, significantly affecting THz signals more than their conventional counter-parts. This research underscores the paramount importance of meticulous alignment in THz communication systems. The profound impact of proper alignment is substantiated through comprehensive measurements conducted using a state-of-the-art measurement setup, facilitating accurate data collection across the 240 GHz to 300 GHz spectrum. These measurements encompass varying angles and distances within an anechoic chamber to eliminate reflections. Through a meticulous analysis of the channel frequency and impulse responses derived from these extensive measurements, this study pioneers quantifiable results, providing an assessment of the effects of beam misalignment in THz frequencies.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.641
Threshold uncertainty score0.210

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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