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Record W2005972201 · doi:10.1109/wcnc.2014.6952331

Joint resource optimization for OFDMA cellular networks with user cooperation and QoS provisioning

2014· article· en· W2005972201 on OpenAlexaff
Roya Arab Loodaricheh, Shankhanaad Mallick, Vijay K. Bhargava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceResource allocationOrthogonal frequency-division multiple accessOptimization problemSubcarrierMathematical optimizationQuality of serviceRelayThroughputFrequency-division multiple accessComputer networkOrthogonal frequency-division multiplexingWirelessPower (physics)Channel (broadcasting)AlgorithmTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

In this paper, a joint resource optimization scheme is designed for orthogonal frequency division multiple access (OFDMA) cellular wireless networks with multi-user cooperation. Joint relay selection, subcarrier allocation and pairing and power allocation algorithms are developed with the objective of maximizing the total capacity of the system considering the quality of service (QoS) requirements of the users. The optimization problem is a mixed integer nonlinear program (MINLP), which is often very difficult to solve in its original form. We provide a novel optimization framework to solve such non-linear optimization problems. The joint relay selection and subcarrier allocation problem is modified to a linear assignment problem and an efficient algorithm is developed to obtain the optimal assignment solution based on the Hungarian method. We propose computationally efficient solution to the joint resource optimization problem via dual decomposition method. Numerical results demonstrate the effectiveness of our proposed scheme.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.620
Threshold uncertainty score0.309

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 head0.018
GPT teacher head0.220
Teacher spread0.201 · 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
Published2014
Admission routes1
Has abstractyes

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