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Record W2969071465 · doi:10.18280/ts.360205

Uplink Sum Rate and Capacity of Hybrid Precoding mmWave Massive MIMO System

2019· article· en· W2969071465 on OpenAlexvenueno aff
Vikash Sachan, Ritesh Kumar Mishra

Bibliographic record

VenueTraitement du signal · 2019
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsPrecodingTelecommunications linkMIMOComputer scienceElectronic engineeringComputer networkTelecommunicationsEngineeringBeamforming

Abstract

fetched live from OpenAlex

Massive multiple input multiple output (MIMO) systems offer an improvement in the uplink sum rate with increasing the number of base station antennas. Massive MIMO system needs the perfect channel state information (perfect CSI) and imperfect channel state information (imperfect CSI) for deriving the achievable sum rate. The Uplink sum rate is derived for the ZF receiver and the MRC receiver with perfect CSI and imperfect CSI. The zero forcing (ZF) receiver outperforms maximal ratio combining (MRC) receiver. With imperfect CSI as the number of users increases the uplink sum rate also increases. The channel capacity is derived for the Millimeter wave (mmWave) MIMO system employing MMSE receiver. The mmWave massive MIMO system requires a large number of radio frequency (RF) chains where as the number of the RF chains increases the capacity of the system also improves.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.559

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 head0.189
Teacher spread0.171 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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