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Energy-Efficient Joint Power Control and Receiver Design for Uplink mmWave-NOMA

2020· article· en· W3044343735 on OpenAlexaff
Ming Zeng, Wanming Hao, Animesh Yadav, Nam‐Phong Nguyen, Octavia A. Dobre, H. Vincent Poor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversité LavalMemorial University of Newfoundland
Fundersnot available
KeywordsTelecommunications linkBeamformingMaximizationPower controlBase stationComputer scienceOptimization problemNomaMathematical optimizationPower (physics)Convex optimizationJoint (building)Efficient energy useEnergy (signal processing)Electronic engineeringRegular polygonTelecommunicationsElectrical engineeringMathematicsEngineeringAlgorithm

Abstract

fetched live from OpenAlex

In this paper, we study the energy efficiency (EE) maximization problem of a millimeter-wave system with non-orthogonal multiple access. We consider a base station equipped with a single radio frequency chain, which serves two uplink users through analog beamforming. The EE maximization problem is formulated under minimal rate requirements, involves a joint design of power and receive beamformer, and is non-convex in nature. To tackle it, we decompose the original problem into two sub-problems: the power control and beam gain allocation sub-problem, and the beamforming sub-problem. The former is solved via alternating optimization, while the latter is tackled by transforming it into a convex optimization problem. Simulation results show the effectiveness of the proposed algorithms and its superiority over orthogonal multiple access.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.202
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2020
Admission routes1
Has abstractyes

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