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Record W2151013122

Sum rate maximization in multi-operator two-way relay networks with a MIMO AF relay via POTDC

2013· article· en· W2151013122 on OpenAlex

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

VenueEuropean Signal Processing Conference · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelayMIMOBeamformingMaximizationComputer scienceMathematical optimizationTransmitter power outputOptimization problemOperator (biology)Relay channelConvex optimizationPower (physics)AlgorithmMathematicsRegular polygonComputer networkTelecommunicationsTransmitter
DOInot available

Abstract

fetched live from OpenAlex

We address the beamforming problem for maximizing the sum rate of a multi-operator two-way relay (TWR) network subject to the constraint on the total relay transmit power. This scenario is also known as relay sharing for multi-way relaying or TWR for multiple operators. The relay is assumed to be equipped with multiple antennas, and it uses the amplify-and-forward relaying strategy. It is shown that the corresponding optimization problem can be represented as a difference of convex functions (DC) programming problem which is NP-hard in general. Nevertheless, we develop an efficient polynomial time algorithm to solve the problem approximately. The performance comparison of the proposed polynomial time DC (POTDC) inspired algorithm to the existing state-of-the-art algorithms demonstrate that the proposed algorithm outperforms the existing algorithms especially in the case of non-symmetric networks.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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