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Record W4401358046 · doi:10.1109/tcomm.2024.3439445

User Sensing in RIS-Aided Wideband mmWave System With Beam-Squint and Beam-Split

2024· article· en· W4401358046 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Communications · 2024
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsWidebandBeam (structure)Computer scienceElectronic engineeringTelecommunicationsOpticsPhysicsEngineering

Abstract

fetched live from OpenAlex

Reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) are considered promising technologies for the sixth generation (6G) wireless communication. The deployment of RIS within the mmWave ISAC system can achieve better communication performance and sensing accuracy. The mmWave band signals can be utilized to enhance transmission rates and available bandwidth significantly. However, the increased size of the RIS array and bandwidth introduces the beam-squint effect, which impacts the performance of RIS-aided communication and sensing. In this paper, we analyze the beam-squint and beam-split effects on a uniform planar array of RIS. Moreover, we derive controllable beam-squint and beam-split ranges based on true-time-delay (TTD) lines and propose RIS-aided sensing schemes with beam-squint and beam-split for a mmWave ISAC system. The proposed schemes can utilize both time-domain and frequency-domain resources for beam scanning, which reduces the time overhead compared to traditional beam scanning schemes. Simulation results illustrate the effectiveness of the proposed RIS-aided user sensing schemes.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.717

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 head0.024
GPT teacher head0.236
Teacher spread0.212 · 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