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Record W2507797364 · doi:10.1109/isit.2016.7541801

On the capacity of a class of dual-band interference channels

2016· article· en· W2507797364 on OpenAlexaff
Subhajit Majhi, Patrick Mitran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTransmitterInterference (communication)Multi-band devicePath lossChannel (broadcasting)Adjacent-channel interferenceMicrowaveChannel capacityFrequency bandCo-channel interferenceElectronic engineeringComputer scienceRadio spectrumTelecommunicationsPhysicsWirelessElectrical engineeringBandwidth (computing)EngineeringAntenna (radio)

Abstract

fetched live from OpenAlex

We consider a two-transmitter two-receiver dual-band Gaussian interference channel (GIC) which is motivated by the simultaneous use of both the conventional microwave band and the unconventional millimeter wave (mm-wave) band in future wireless networks where the traditional microwave band is complemented by additional spectrum in the mm-wave band. A key modeling feature of the mm-wave band is that due to severe path loss and relatively small wavelength, it must be used with highly directional antennas, and thus the transmitter is able to transmit to its intended receiver with negligible to no interference to other receivers. For this model, we derive some sufficient conditions on the channel gains under which the capacity of this type of dual-band GIC is determined. Specifically, these conditions are classified as when microwave band channel gains have (a) weak interference, i.e., both the cross channel gains are less than 1 and (b) mixed interference, i.e., only one of the cross channel gains is less than 1, while the channel gains in the dual-band GIC satisfy certain additional conditions in each case.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.295

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 head0.044
GPT teacher head0.215
Teacher spread0.171 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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