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Record W2964323994 · doi:10.1109/allerton.2011.6120222

Incremental relaying for the Gaussian interference channel with a degraded broadcasting relay

2011· article· en· W2964323994 on OpenAlexaff
Lei Zhou, Wei Yu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelayRelay channelTransmitterComputer scienceChannel (broadcasting)Interference (communication)Broadcasting (networking)Topology (electrical circuits)Transmission (telecommunications)Computer networkGaussianTelecommunicationsElectronic engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper studies a two-user Gaussian interference channel with an in-band-reception and out-of-band-transmission relay, where the link between the relay and the two receivers is modeled as a degraded broadcast channel. It is shown that a generalized hash-and-forward (GHF) relaying strategy can achieve the capacity region of this channel to within a constant number of bits in a certain weak relay regime, where the transmitter-to-relay link gains are not unboundedly stronger than the interference links between the transmitters and the receivers. In such a regime, the GHF relaying strategy can be performed in an incremental fashion, where the relay message to one receiver is a degraded version of the message to the other receiver. A generalized-degree-of-freedom analysis reveals that in the symmetric channel setting, each common relay bit can improve the sum rate roughly by about one bit or two bits asymptotically, and the rate gain can be thought as coming solely from the improvement of the common message rates.

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: Empirical · Consensus signal: none
Teacher disagreement score0.867
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.066
GPT teacher head0.241
Teacher spread0.175 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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