MétaCan
Menu
Back to cohort

Characterization and compensation of DC offset on adaptive MIMO direct conversion transceivers

2009· article· en· W2104864640 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOffset (computer science)DC biasTransmitterMIMOTransceiverControl theory (sociology)Computer scienceGaussianElectronic engineeringChannel state informationBit error rateAlgorithmChannel (broadcasting)WirelessVoltageEngineeringTelecommunicationsPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

The effect of DC offset on multi-input multi-output (MIMO) direct transceivers with adaptive modulation (AM) is discussed in this paper. A variable-rate variable-power (VRVP) AM system with perfect channel state information (P-CSI) at both the transmitter and receiver in a MIMO scenario is considered. The DC offset is modeled as a zero mean complex Gaussian distributed random variable. By this modeling of the DC offset, the analytical expression for degraded bit error rate has been derived. To derive this analytical expression, some approximation has been made. The good agreement between the analytical and simulation results shows that proper approximation has been made and confirms the accuracy of the analytical expressions. Moreover, an approach to compensate the DC offset in these systems is introduced. The compensation technique is applied and the improvement has been examined.

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: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.360

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 head0.007
GPT teacher head0.186
Teacher spread0.178 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Network OptimizationFrench-language works237,207