MétaCan
Menu
Back to cohort
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 OpenAlex
Xinjie Wang, Nan Yang, Hao Zhang, Tiep M. Hoang, T. Aaron Gulliver

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.901
Threshold uncertainty score0.566

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 communication0.0000.000
Open science0.0030.002
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 head0.062
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