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Record W2977180838 · doi:10.1109/isit.2019.8849640

Achievable Rates and Outer Bounds for Full-Duplex Relay Broadcast Channel with Side Message

2019· article· en· W2977180838 on OpenAlexaff
Kaiming Shen, Reza K. Farsani, Wei Yu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConverseRelayRelay channelTelecommunications linkComputer networkComputer scienceChannel (broadcasting)GaussianTopology (electrical circuits)Linear network codingWirelessCoding (social sciences)MathematicsTelecommunicationsStatisticsPhysicsCombinatoricsGeometry

Abstract

fetched live from OpenAlex

This paper examines the achievable rate region and the converse of a full-duplex relay broadcast channel with three independent messages: from the source to the relay, from the source to the destination, and from the relay to the destination. We are motivated to study this channel, because it models a full-duplex wireless cellular network in which the uplink user also wishes to send an independent device-to-device message to the downlink users. For the discrete memoryless channel case, we incorporate Marton's broadcast coding to obtain a new achievable rate region which is larger than previous rate regions. We further propose a tighter converse than the cut-set bound. For the Gaussian scalar channel case, we show that by using one of two rate-splitting schemes depending on the channel condition, we can already achieve the capacity region of this particular relay broadcast channel to within a constant gap. The proposed scheme outperforms the benchmark methods in terms of the symmetric generalized degree-of-freedom.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.219
Teacher spread0.209 · 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 designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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