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Record W1877527202 · doi:10.1109/vetecs.2004.1388929

Linear space-time transmitter and receiver processing and scheduling for the MIMO broadcast channel

2005· article· en· W1877527202 on OpenAlexaff
David Mazzarese, Witold A. Krzymień

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransmitterMIMOComputer scienceChannel (broadcasting)PrecodingThroughputScheduling (production processes)Base stationElectronic engineeringTopology (electrical circuits)Computer networkTelecommunicationsWirelessMathematicsElectrical engineeringEngineeringMathematical optimization

Abstract

fetched live from OpenAlex

We consider a MIMO broadcast channel where the transmitter and the receivers are equipped with multiple antennas. We propose a new scheme using linear processing at the transmitter and at the receivers to jointly diagonalize the channel so that two users can receive data simultaneously. It is also applicable when the users are equipped with different numbers of antennas, and it is able to take advantage of all the spatial degrees of freedom as long as the overall number of all receive antennas is greater than or equal to the number of transmit antennas. We show that the maximum throughput achievable with our scheme is larger than the maximum throughput achievable by transmitting to a single user at a time and by transmitting to several users simultaneously with receiver processing only. The proposed strategy achieves a large portion of the two-user sum-capacity when the base station has two transmit antennas. We provide an asymptotic analysis and simulation results to illustrate our analysis.

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)0.002
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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