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Compressive Sensing-Based Channel Estimation for MIMO OTFS Systems

2023· article· en· W4385688967 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMatching pursuitCompressed sensingAlgorithmMIMOChannel (broadcasting)Block (permutation group theory)Computer scienceOrthogonal frequency-division multiplexingMIMO-OFDMModulation (music)Minimum mean square errorFrequency domainMathematicsTelecommunicationsComputer visionStatisticsAcoustics

Abstract

fetched live from OpenAlex

Orthogonal time frequency space (OTFS) modulation is a novel two-dimensional modulation technique that performs in the delay-Doppler (DD) domain. In this work, we present a new compressive sensing (CS)-based algorithm for estimating the channel in the DD domain for multiple-input multiple-output (MIMO) OTFS systems. Exploiting the property that the MIMO channel in the DD domain exhibits structured sparsity, we first obtain a row-block sparse formulation for channel estimation (CE) problem. Then, we propose a row-block orthogonal matching pursuit (RBOMP) algorithm to estimate the channel. Computer simulations demonstrate that the proposed algorithm enhances the estimation accuracy compared with the conventional minimum mean squared error (MMSE)-based and the existing CS-based CE techniques.

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.

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: none
Teacher disagreement score0.973
Threshold uncertainty score0.359

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 head0.026
GPT teacher head0.255
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

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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