MétaCan
Menu
Back to cohort
Record W3021372465 · doi:10.1109/tvt.2020.2991377

Localization Error Bounds for 5G mmWave Systems Under I/Q Imbalance

2020· article· en· W3021372465 on OpenAlexafffund
Fariba Ghaseminajm, Zohair Abu‐Shaban, Salama Ikki, Henk Wymeersch, Craig R. Benson

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransmitterTransceiverQuadrature amplitude modulationAmplitudeQuadrature (astronomy)Electronic engineeringComputer scienceQAMDegradation (telecommunications)Amplitude modulationCommunications systemControl theory (sociology)Bit error rateTelecommunicationsEngineeringFrequency modulationRadio frequencyPhysicsWirelessDecoding methodsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

Location awareness is expected to play a significant role in 5G millimeter-wave (mmWave) communication systems. One of the basic elements of these systems is quadrature amplitude modulation (QAM), which has in-phase and quadrature (I/Q) modulators. It is not uncommon for transceiver hardware to exhibit an imbalance in the I/Q components, causing degradation in data rate and signal quality. Under an amplitude and phase imbalance model at both the transmitter and receiver, 2D positioning performance in 5G mmWave systems is considered. Towards that, we derive the position and orientation error bounds and study the effects of the I/Q imbalance parameters on the derived bounds. The numerical results reveal that I/Q imbalance impacts the performance similarly, whether it occurs at the transmitter or the receiver, and can cause a degradation up to 12% in position and orientation estimation accuracy.

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)0.004
metaresearch head score (Gemma)0.025
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.239
Teacher spread0.217 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Vehicular TechnologySame topicPower Line Communications and NoiseFrench-language works237,207