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Record W2408026441 · doi:10.1109/icassp.2016.7472334

Coordinated uplink scheduling and beamforming for wireless cellular networks via sum-of-ratio programming and matching

2016· article· en· W2408026441 on OpenAlexaff
Kaiming Shen, Wei Yu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTelecommunications linkComputer scienceBeamformingScheduling (production processes)Cellular networkWireless networkQuadratic programmingMathematical optimizationOptimization problemWirelessComputer networkAlgorithmMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes a joint uplink user scheduling and beam-forming algorithm for a multiple-antenna wireless cellular network. We show that coordinated optimization across the cells can significantly alleviate intercell interference, thereby improving the cell-edge rates in a multicell network. Unlike the downlink case, coordinating uplink transmission in a multicell network is significantly more challenging, because uplink interference depends strongly on the schedule and beamformers of neighboring cells. The main contribution of this paper is the recasting of the problem in terms of sum-of-ratio programming and a subsequent quadratic reformulation which allows scheduling and beamforming to be optimized through solving a matching problem. This problem reformulation also provides a new interpretation of the well-known weighted minimum mean square error (WMMSE) algorithm. Simulation results show that the proposed approach significantly outperforms both the WMMSE algorithm and the existing uncoordinated scheduling approach.

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 score0.730
Threshold uncertainty score0.466

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 head0.006
GPT teacher head0.207
Teacher spread0.200 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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