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Turbo Receiver for Polar-Coded OFDM systems with unknown CSI

2020· article· en· W3108119177 on OpenAlexaff
Siyu Zhang, Behnam Shahrrava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceOrthogonal frequency-division multiplexingFadingTurbo codeTurboDecoding methodsChannel state informationAlgorithmBit error rateTurbo equalizerRadio receiver designDetectorElectronic engineeringChannel (broadcasting)TelecommunicationsConcatenated error correction codeWirelessEngineeringBlock code

Abstract

fetched live from OpenAlex

In this paper, a turbo receiver for polar-coded orthogonal frequency division multiplexing(OFDM) systems for frequency-selective fading channels with unknown channel state information(CSI) is proposed. The receiver iteratively exchanges soft information between an expectation-maximization(EM) symbol detector and a soft polar decoder that is based on the belief propagation(BP) algorithm. By utilizing such receiver, the error-correcting performance of the system can be significantly improved even with unknown CSI. Simulation results show that by using the proposed turbo receiver, around 5dB coding gain at a bit-error rate of 5 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> can be obtained compared to the receiver that detects symbols and implements decoding separately with unknown CSI.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.506

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 head0.239
Teacher spread0.212 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2020
Admission routes1
Has abstractyes

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