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Record W4403390310 · doi:10.1109/twc.2024.3475296

Holographic MIMO NOMA Communications: A Power Saving Design

2024· article· en· W4403390310 on OpenAlexaff
Zeyu Sun, Yindi Jing

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNomaComputer scienceMIMOPower (physics)TelecommunicationsWirelessElectronic engineeringTelecommunications linkBeamformingEngineeringPhysics

Abstract

fetched live from OpenAlex

The downlink non-orthogonal multiple access (NOMA) transmissions from a holographic multi-input multi-output surface (HMIMOS) transmitter to multiple single-antenna users are investigated in this work. And we focus on the power saving design when the HMIMOS has a massive number of elements. For single-cluster NOMA transmissions implemented by a single-RF-chain HMIMOS-based transmitter, the required transmit power to maintain the quality of service (QoS) of all users are derived and two holographic beamforming schemes aiming at minimizing the required transmit power are developed. For multi-cluster NOMA transmissions implemented by a multi-RF-chain HMIMOS-based transmitter, a two-layer partitioning problem is formulated and solved to minimize the power consumption. Numerical results are provided to validate our theoretical analysis. It is shown that the NOMA scheme has lower power consumption than orthogonal multiple access (OMA) based multi-user transmission schemes when the number of RF chains is limited, and our proposed holographic beamforming schemes achieve lower power consumption than other holographic beamforming schemes.

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 categoriesMeta-epidemiology (narrow)
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 score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.000
Research integrity0.0000.002
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 head0.040
GPT teacher head0.279
Teacher spread0.240 · 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.

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
Published2024
Admission routes1
Has abstractyes

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