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Record W2428777316 · doi:10.1109/access.2016.2580680

Non-Coherent Open-Loop MIMO Communications Over Temporally-Correlated Channels

2016· article· en· W2428777316 on OpenAlexafffund
Jorge Cabrejas, Sandra Roger, Daniel Calabuig, Yaser M. M. Fouad, Ramy H. Gohary, José F. Monserrat, Halim Yanıkömeroğlu

Bibliographic record

VenueIEEE Access · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsCarleton University
FundersEuropean Regional Development FundMinistry of Economy, Trade and IndustryMinisterio de Economía y CompetitividadOntario Ministry of Economic Development and Innovation
KeywordsMIMORayleigh fadingComputer scienceDifferential (mechanical device)Transmission (telecommunications)3G MIMOChannel (broadcasting)Electronic engineeringFadingChannel state informationBlock codeMulti-user MIMOTelecommunicationsDecoding methodsWirelessPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper investigates the use of non-coherent communication techniques for open-loop transmission over temporally-correlated Rayleigh-fading MIMO channels. These techniques perform data detection without knowing the instantaneous channel coefficients. Three non-coherent Multiple Input Multiple Output (MIMO) schemes, namely, differential unitary space-time modulation, differential space-time block code, and Grassmannian signaling, are compared with several state-of-the-art training-based coherent schemes. This paper shows that the non-coherent schemes are meaningful alternatives to training-based communication, specially as the number of transmit antennas increases. In particular, for more than two transmit antennas, non-coherent communication provides a clear advantage in medium to high mobility scenarios.

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 score0.656
Threshold uncertainty score0.850

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 communication0.0000.001
Open science0.0050.001
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 head0.053
GPT teacher head0.344
Teacher spread0.291 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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