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Record W2037360800 · doi:10.1109/tvt.2014.2335209

Distributed Precoding for OFDM in Two-Way Relaying Communications

2014· article· en· W2037360800 on OpenAlexafffund
Hongzhong Yan, Ha H. Nguyen, Jian Su

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPrecodingRician fadingDiversity gainOrthogonal frequency-division multiplexingDiversity schemeRayleigh fadingComputer scienceFadingCoding gainZero-forcing precodingElectronic engineeringCoding (social sciences)TelecommunicationsAlgorithmMathematicsEngineeringMIMOStatisticsDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper is concerned with the design of distributed precoding in the multiple access (MA) phase for two-way relaying communication (TWRC) systems using OFDM. The error probability analysis is conducted to establish the diversity and coding gains for three error types in the MA phase. Then, the design criteria of distributed precoding to achieve the maximum diversity and coding gains are given. The frequency-grouped linear constellation precoding (F-GLCP) is first investigated and shown not to be able to achieve the maximum diversity gain under type-3 errors. A novel frequency-time GLCP (FT-GLCP) that performs precoding in both the frequency and time domains is then proposed. It is proved that the proposed FT-GLCP is able to achieve the maximum diversity gain under type-3 errors while maintaining the maximum diversity and coding gains under type-1 and type-2 errors. To corroborate the theoretical analysis, simulation results are provided to show the advantage of the proposed FT-GLCP over other schemes in both Rayleigh and Rician fading channels.

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 score0.940
Threshold uncertainty score0.663

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.033
GPT teacher head0.300
Teacher spread0.267 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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