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Record W3012983481 · doi:10.1186/s13638-020-1637-4

Channel estimation based on the PSS-MUSIC for millimeter-wave MIMO systems equipped with co-prime arrays

2020· article· en· W3012983481 on OpenAlexaff
Shufeng Li, Guangjing Cao, Libiao Jin, Hongda Wu

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2020
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsYork University
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsComputer scienceChannel (broadcasting)Prime (order theory)Path (computing)AlgorithmAperture (computer memory)MIMODirection of arrivalExtremely high frequencyComputational complexity theoryTelecommunicationsAcousticsMathematicsPhysicsAntenna (radio)

Abstract

fetched live from OpenAlex

Abstract The millimeter-wave channel estimation problem can be solved by estimating the path directions and the path gains. The previous schemes are nearly all based on the uniform linear arrays (ULAs). However, compared with the ULAs, the co-prime array can use fewer array elements to realize larger array aperture, which is beneficial to improve the performance of arrival direction estimation contributing to the estimation performance of angle of arrival (AOA) and angle of departures (AOD). Encouraged by the property of co-prime arrays, a novel channel estimation scheme is proposed for two-dimensional (2-d) co-prime arrays. For each path direction, multiple peaks are generated in the spatial spectrum of each subarray and by selecting over any limited sector to search for an arbitrary peak, which can recover the rest peaks. Then by comparing the peaks of the two subarrays, the common peaks are the correct path direction. Compared with the total-spectrum search (TSS) method, this method effectively reduces the complexity. Simulation results show that the proposed algorithm can reduce the computational complexity and maintain accurate channel estimation.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.106
GPT teacher head0.291
Teacher spread0.185 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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