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Record W2907218180 · doi:10.1109/mvt.2018.2866306

Noncoherent MIMO Signaling for Block-Fading Channels: Approaches and Challenges

2019· article· en· W2907218180 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 Vehicular Technology Magazine · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsFadingMIMOChannel (broadcasting)Block (permutation group theory)Computer scienceWirelessElectronic engineeringComputer networkTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

The majority of existing wireless communication systems relies on training-based signaling, i.e., schemes that rely on accurate channel estimates for detecting the transmitted symbols. Training requires its own hardware and algorithms and consumes a significant portion of the channel coherence interval, which is typically short in vehicular communications. The drawbacks of training can be alleviated by using noncoherent signaling schemes, which dispense with the training phase altogether. We will focus on a particular class of such schemes, i.e., Grassmannian signaling. This scheme is optimal for high signal-to-noise ratio (SNR) communication over noncoherent block-fading channels, which are likely to arise in many future networks.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
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 studies0.0000.000
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 head0.039
GPT teacher head0.237
Teacher spread0.198 · 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