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Record W2097364865 · doi:10.1109/tvt.2005.844640

Multiantenna Capacities of Waveguide and Cavity Channels

2005· article· en· W2097364865 on OpenAlexaff
Sergey Loyka

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOrthogonalityMIMOWaveguideSpatial correlationChannel capacityTransmission (telecommunications)Channel (broadcasting)Electronic engineeringModalTopology (electrical circuits)PhysicsOpticsMathematicsComputer scienceEngineeringTelecommunicationsMaterials scienceElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

Multiple-input-multiple-output (MIMO) capacity of waveguide and cavity channels is investigated using the modal expansion technique. Rectangular and circular waveguides and cavities are studied in details. Approximate expressions for the number of modes and for the capacity are given. A MIMO system architecture is suggested for a waveguide channel, which achieves the full capacity by making use of the mode orthogonally (or near orthogonality) using an eigenmode modulator at the Tx end and a spatial correlation receiver at the Rx end. Various practical limitations (e.g., nonideal waveguides and modulators, using discrete sensors instead of continuous, one-dimensional sensors instead of two-dimensional, etc.) and their impact on the capacity are discussed. It is demonstrated that long cavities are equivalent to waveguides in terms of capacity. The concept of spatial capacity is introduced to characterize the limits on the transmission rates that are due to both electromagnetic and information-theoretic considerations, which can be evaluated in a closed form for ideal waveguides and cavities. It follows that the traditional single-mode transmission is optimum in terms of capacity in the small signal-to-noise ratio region only.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.716

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 head0.194
Teacher spread0.186 · 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 designBench or experimental
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

Citations38
Published2005
Admission routes1
Has abstractyes

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