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Record W3216031482 · doi:10.1109/5gwf52925.2021.00015

All-Analog Structures for AF Relaying in mmWave Massive MIMO Systems

2021· article· en· W3216031482 on OpenAlexaff
Soumya Khare, Alireza Morsali, Benoı̂t Champagne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsMcGill University
Fundersnot available
KeywordsBasebandElectronic engineeringBeamformingMIMOComputer scienceRadio frequencyAnalog signal processingRelayDuplex (building)Power (physics)Signal processingEngineeringTelecommunicationsDigital signal processing

Abstract

fetched live from OpenAlex

Hybrid analog/digital (A/D) beamforming is preferred in the implementation of relays for mmWave massive multiple-input multiple-output (mMIMO) systems due to the smaller number of radio frequency (RF) chains required compared to fully digital (FD) beamforming. Although the hybrid structure reduces system cost and power consumption, it still requires expensive baseband processing while the unit-modulus constraint in the analog domain limits system performance. In this paper, motivated by these considerations, we propose and investigate the design of all-analog structures for amplify-and-forward (AF) half-duplex relaying in mMIMO systems, which are comprised of the conventional RF components, including: power dividers/combiners, phase-shifters, and delay elements. For the proposed structures, we consider a constrained data rate maximization problem and formulate the AF relay designs. The ensuing solution is not bound to the unit-modulus constraint and does not require RF chains for conversion between analog and baseband domains. Simulation results demonstrate that using the proposed analog structures for AF half-duplex relaying in mMIMO communications can achieve the same performance as the optimal FD relay design.

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 score0.064
Threshold uncertainty score0.499

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 head0.030
GPT teacher head0.259
Teacher spread0.229 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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