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Record W3101768914 · doi:10.1109/tcomm.2020.3038772

Dual Pilot Scheme (DPS) and Its Application in Massive MIMO

2020· article· en· W3101768914 on OpenAlexafffund
Abdelmalik Nasser Ali Aljalai, Chen Feng, Victor C. M. Leung, Rabab Ward

Bibliographic record

VenueIEEE Transactions on Communications · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMIMORayleigh fadingComputer scienceChannel state informationWirelessInterference (communication)FadingChannel (broadcasting)PrecodingMulti-user MIMOElectronic engineeringAlgorithmComputer networkEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The pilot scheme currently used in 5th generation (5G) cellular networks assigns the same set of orthogonal pilot signals to all cells. This results in inter-cell interference, also known as pilot contamination, which can significantly degrade performance, especially in massive multi-input multi-output (MIMO) systems. To mitigate this interference, we propose a novel Dual Pilot Scheme (DPS) that assigns a slightly modified set of nearly-orthogonal pilot signals. DPS is a general scheme that can be implemented in any wireless communication system, including 5G and beyond. We demonstrate the integration of DPS in a massive MIMO system in both microscopic and macroscopic levels and analytically prove that DPS enables more accurate estimates of the channel state information in the minimum mean-squared error sense, under the independent identically distributed (i.i.d.) and the correlated Rayleigh fading wireless communication channel models. We further validate and demonstrate the advantages of DPS over various channel models of massive MIMO 5G technology by extensive simulations.

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: Methods · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.685

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 head0.035
GPT teacher head0.259
Teacher spread0.223 · 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
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

Citations2
Published2020
Admission routes2
Has abstractyes

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