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Sensing via Orthogonal Time Frequency Space Signalling and Reconfigurable Intelligent Surface

2022· article· en· W4312465480 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

Venue2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · 2022
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceTransmission (telecommunications)Electronic engineeringRadarFrequency modulationModulation (music)Range (aeronautics)Base stationTunable metamaterialsOrthogonal frequency-division multiplexingReal-time computingAcousticsTelecommunicationsEngineeringRadio frequencyChannel (broadcasting)OpticsPhysics

Abstract

fetched live from OpenAlex

Orthogonal time frequency space (OTFS) modulation is a new technique that transmits symbols in delay-Doppler plane and can be used for both communication and sensing. A Reconfigurable Intelligent Surface (RIS) is an array of elements that can be controlled to produce desirable behavior over the signals and enhance communication. In this paper, we utilize RIS to increase the resolution of the OTFS-based radar systems, while decreasing the transmission time of base station. We solve the problem of detection by sparse recovery and treating it as an inverse problem. Our simulation results show the effectiveness of the RIS in increasing the resolution of detection over the range and velocity of the targets.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.013
GPT teacher head0.241
Teacher spread0.229 · 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