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Record W1984119615 · doi:10.1109/icspcs.2013.6723916

Joint design of user power allocation and relay beamforming in two-way MIMO relay networks

2013· article· en· W1984119615 on OpenAlexaff
Ha Hoang Kha, Hoang Duong Tuan, Ha H. Nguyen, H. H. M. Tam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRelayBeamformingComputer scienceOptimization problemMIMOMathematical optimizationJoint (building)WirelessTransmitter power outputConvex optimizationPower (physics)Relay channelIterative methodAntenna (radio)Regular polygonAlgorithmComputer networkMathematicsEngineeringTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper is concerned with the joint optimization of user power allocation and distributed relay beamforming in two-way wireless relay networks in which multiple multi-antenna relays assist multiple single-antenna users. The design aims at maximizing the minimum information rate among user pairs subject to various practical power constraints at the users and relays. Since the nonconvex structure of the problem is highly complicated, the existing approaches to relax the design problem into a convex program do not appear to be applicable. To solve this challenging nonconvex optimization problem, we introduce auxiliary variables and cast the design problem into a d.c.(difference of convex functions) program. Then, we develop an efficient iterative algorithm of sequential convex optimization to obtain the optimized solutions. Extensive simulation results demonstrate that our proposed joint design outperforms the only relay beamforming design.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.261
Teacher spread0.226 · 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
GenreMethods

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

Citations7
Published2013
Admission routes1
Has abstractyes

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