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Record W4323338389 · doi:10.1109/access.2023.3253680

SNR Threshold-Based Relay Association and Random Phase Rotation for Cooperative Communication

2023· article· en· W4323338389 on OpenAlexaff
Jingon Joung, Soobum Park, Ji-Myung Oh, Eui–Rim Jeong

Bibliographic record

VenueIEEE Access · 2023
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsNexen (Canada)
FundersInstitute for Information and Communications Technology PromotionDefense Acquisition Program AdministrationMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningNational Research Foundation
KeywordsRelayBit error rateComputer scienceNode (physics)Signal-to-noise ratio (imaging)Channel (broadcasting)Frame (networking)Communications systemModulation (music)Code (set theory)Phase (matter)Rotation (mathematics)Electronic engineeringTopology (electrical circuits)Computer networkTelecommunicationsElectrical engineeringEngineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

In this study, a cooperative communication system that employs multiple decode-and-forward relay nodes (RNs), in which the associated/active RNs perform phase rotation of the regenerated signals before retransmitting them to a destination node (DN), is examined. In the first phase, i.e., communication from a source node to RNs, a received signal-to-noise ratio (SNR) threshold-based RN association method is proposed. The optimal SNR thresholds are designed to maximize the bit-error-rate (BER) performance at the DN under various communication environments, such as modulation types and channel code rates. Furthermore, the number of phase rotations (PRs) in a frame is examined. Intensive numerical results show that more PRs in a frame provide better BER performance at the DN, irrespective of the communication environments. This study provides a valuable guideline for designing practical cooperative networks with multiple decode-and-forward RNs with PRs.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.078
GPT teacher head0.379
Teacher spread0.301 · 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
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

Citations6
Published2023
Admission routes1
Has abstractyes

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