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Record W2972367472 · doi:10.1109/access.2019.2940078

On the Multiplexing Capability of mmWave Doubly-Massive MIMO Systems in LOS Environments

2019· article· en· W2972367472 on OpenAlexaff
Dian‐Wu Yue, Ha H. Nguyen

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBeamformingMultiplexingMIMOSpatial multiplexingTelecommunications linkComputer scienceTransmitter power outputArray gainAntenna (radio)Base stationPrecodingElectronic engineeringMulti-user MIMOTopology (electrical circuits)Antenna arrayComputer networkTelecommunicationsTransmitterElectrical engineeringEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper is concerned with the potential of spatial multiplexing in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) downlink system in which the base station (BS) is equipped with multiple distributed massive antenna subarrays to serve multiple users. The paper first introduces the concept of multiplexing gain when the number of antennas goes to infinity and the transmit power is finite. Assume that each of the user terminals is equipped with a large antenna array, the paper focuses on the simple and cost-effective analog beamforming scheme for the MIMO system operating in mmWave line-of-sight propagation environments. We obtain simple and approximate expressions for the sum rate of the system based on asymptotic analysis in the limit of the numbers of antennas at the BS and at the user terminals when the total transmit power is fixed or scaled down. The approximate expressions show that the employed analog beamforming scheme can achieve a multiplexing gain that is equal to the product of the number of subarrays at the BS and the number of users when the total transmit power is fixed, whereas the multiplexing gain decreases proportionally to such a product when the total transmit power is scaled down. Extensive numerical results are also provided to corroborate analytical results.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.346

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 head0.033
GPT teacher head0.253
Teacher spread0.220 · 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
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

Citations6
Published2019
Admission routes1
Has abstractyes

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