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Record W2080699736 · doi:10.1109/glocom.2006.794

WLC31-2: Capacity of MIMO Rician Fading Channels with Transmitter and Receiver Channel State Information

2006· article· en· W2080699736 on OpenAlexaff
Amine Maaref, Sonia Aı̈ssa

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsRician fadingMIMOTransmitterChannel state informationFadingChannel (broadcasting)Channel capacityComputer scienceErgodic theoryTopology (electrical circuits)PrecodingMathematicsTelecommunicationsElectronic engineeringControl theory (sociology)WirelessEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

This paper investigates the capacity of multiple-input multiple-output (MIMO) wireless systems when instantaneous channel state information (CSI) is available at both the transmitter and the receiver in a line-of-sight Rician fading environment. Specifically, an infinite series representation for the ergodic capacity of uncorrelated Rician fading MIMO channels is derived, assuming both transmitter and receiver CSI and a specular component of arbitrary rank. The ergodic capacity and its associated outage probability are expressed in terms of a cutoff level capturing the optimal eigen-mode power and rate adaptation. Moreover, an equation from which the cutoff value can be solved for numerically is derived for arbitrary numbers of transmit and receive antennas.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.168
Teacher spread0.162 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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