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Two-Phase Channel Estimation for UPA-Type RIS-Aided Multi-User mmWave Systems with Reduced Pilot Overhead and Error Propagation

2023· article· en· W4387871164 on OpenAlexaff
Zhendong Peng, Cunhua Pan, Gui Zhou, Hong Ren

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverhead (engineering)Channel (broadcasting)Computer scienceElectronic engineeringBase stationPhase (matter)AlgorithmReal-time computingTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, an efficient two-phase channel estimation scheme with reduced pilot overhead and error propagation is proposed for a uniform planar array (UPA)-type reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. In Phase I, based on the carefully designed RIS phase shift matrix, all users jointly transmit the pilot signals to estimate the correlation factors between different propagation paths of the common RIS-base station (BS) channel, which facilitates a significant MU diversity gain. Then, in Phase II, with the constructed ambiguous RIS-BS channel composed of the correlation factors obtained in the previous phase, each user independently sends a few pilots to estimate their own ambiguous user-RIS channel so as to obtain the entire cascaded channel. Simulation results validate that the proposed algorithm outperforms the existing algorithms in terms of both pilot overhead and estimation accuracy, and that its estimation performance improves as the number of users increases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.065
GPT teacher head0.328
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

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