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Record W2507003164 · doi:10.1109/icc.2016.7511290

Polar codes for noncoherent MIMO signalling

2016· article· en· W2507003164 on OpenAlex
Philip R. Balogun, Ian Marsland, Ramy H. Gohary, Halim Yanıkömeroğlu

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsDecoding methodsMIMOComputer scienceWirelessErgodic theoryBlock codeAlgorithmCoding (social sciences)Turbo codePolarFadingGrassmannianTheoretical computer scienceElectronic engineeringTelecommunicationsMathematicsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Polar codes, ever since their introduction, have been shown to be very effective for various wireless communication channels. This together with their relatively low implementation complexity has made polar codes an attractive coding scheme for wireless communications. On the other hand, within the realm of non-coherent wireless MIMO communication, Grassmannian signalling has been shown to approach the ergodic capacity of frequency-flat block fading channels. In this paper, a novel methodology for designing polar codes that works effectively with Grassmannian signalling and a novel set partitioning algorithm for Grassmannian constellations are proposed. We compare the error rate performance of our design with that of existing schemes and show that a gain of over 1 dB over the previously known best technique, which is based on turbo codes, is possible, at much lower decoding complexity.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score0.244

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 science0.0010.000
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 head0.027
GPT teacher head0.282
Teacher spread0.254 · 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

Quick stats

Citations10
Published2016
Admission routes1
Has abstractyes

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