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Record W3025561490 · doi:10.1109/tvt.2020.2993900

Delayed Bit Interleaved Coded Sparse Code Multiple Access

2020· article· en· W3025561490 on OpenAlexaff
Zeina Mheich, Zilong Liu, Pei Xiao, Amine Maaref

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsHuawei Technologies (Canada)
FundersEngineering and Physical Sciences Research CouncilUniversity of Surrey
KeywordsComputer scienceCode wordBit error rateDecoding methodsTransmitterNetwork packetEncoderCode (set theory)Channel (broadcasting)Turbo codeElectronic engineeringAlgorithmComputer engineeringComputer networkEngineering

Abstract

fetched live from OpenAlex

Targeting to provide reliable short-packet communications with tens of bits in machine-type networks, we investigate a novel sparse code multiple access (SCMA) scheme called delayed bit-interleaved coded SCMA (DBIC-SCMA). At the transmitter side, a delay module is introduced between each channel encoder and SCMA mapper such that data bits from different channel codewords can be mapped to an identical SCMA codeword. We present the main components and principles for the transmitter of DBIC-SCMA, followed by a pre-decoding assisted receiver design which exploits systematic and rate-adaptive properties of certain channel codes. Our simulation results show that the proposed DBIC-SCMA leads to significant improvements in error rate performance over the classical BIC-SCMA scheme.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.258
Teacher spread0.227 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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