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Record W2901392065 · doi:10.1109/tvt.2018.2880798

Multi-Resolution Multicasting Using Grassmannian Codes and Space Shift Keying

2018· article· en· W2901392065 on OpenAlexaff
Mohamed A. ElMossallamy, Karim G. Seddik, Ramy H. Gohary

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsCodebookChannel state informationTransmitterComputer scienceTransmission (telecommunications)MulticastAlgorithmDetectorGrassmannianMIMODecoding methodsKeyingElectronic engineeringChannel (broadcasting)TelecommunicationsMathematicsComputer networkEngineeringWireless

Abstract

fetched live from OpenAlex

In this paper, we develop a novel layered coding scheme for the multiple-input multiple-output multicast channel. In this scheme, information is encoded in two layers, a base low-resolution (LR) layer and refining high-resolution (HR) one. The LR layer is encoded using Grassmannian noncoherent codes and the HR layer is encoded in the indices of the active transmitter antennas using the so-called space shift keying modulation. An efficient algorithm is proposed to optimize the HR codebook. The LR information can be detected noncoherently without invoking any channel state information (CSI), whereas the HR information must be detected coherently and, thus, requires accurate CSI. Hence, receivers with perfect CSI can decode both the LR and HR information, whereas those with no CSI can only decode the LR information. For receivers with accurate CSI, we propose a computationally efficient two-step detector and we show that the noncoherent detector performance is not affected by the transmission of the incremental HR information encoded in the transmit antenna indices.

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 categoriesMeta-epidemiology (narrow)
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 score0.475
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
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.268
Teacher spread0.242 · 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.

Study designSimulation or modeling
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

Citations4
Published2018
Admission routes1
Has abstractyes

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