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Record W2292514988 · doi:10.1049/iet-com.2015.0110

Generalised selection at multi‐antenna sources in two‐way relay networks

2016· article· en· W2292514988 on OpenAlexaff
Xinjie Wang, Nan Yang, Hao Zhang, Tiep M. Hoang, T. Aaron Gulliver

Bibliographic record

VenueIET Communications · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsRelayComputer scienceSelection (genetic algorithm)Antenna (radio)TelecommunicationsComputer networkArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

A generalised selection transmission (GST) and generalised selection combining (GSC) scheme is proposed for two‐way relay networks where two multi‐antenna sources exchange information via a single‐antenna relay. New exact and asymptotic expressions are derived for the outage probability and symbol error rate (SER) in Rayleigh fading. Moreover, a tight upper bound on the ergodic sum‐rate is presented. These results are used to demonstrate that the proposed GST/GSC scheme preserves the full diversity order, which equals the minimum number of antennas at the two sources. It is also shown that the impact of the number of selected antennas lies in the array gain only. Furthermore, the GST/GSC scheme significantly improves the performance relative to single‐antenna selection, and only incurs a negligible reduction in performance relative to all‐antenna beamforming. Finally, the optimal relay location that minimises the SER is determined analytically. It is observed that the optimal relay location shifts towards one source when the number of selected or available antennas at the other source increases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.062
GPT teacher head0.318
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2016
Admission routes1
Has abstractyes

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