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Record W2086801507 · doi:10.1155/2010/572675

Efficient Transmission Schemes for Multiuser MIMO Downlink with Linear Receivers and Partial Channel State Information

2010· article· en· W2086801507 on OpenAlexafffund
Mohsen Eslami, Witold A. Krzymień

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of AlbertaAlberta Environment and Protected Areas
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer sciencePrecodingTelecommunications linkChannel state informationBase stationMIMOComputer networkTransmission (telecommunications)Scheduling (production processes)Dirty paper codingHandshakingTransmitterChannel (broadcasting)WirelessTelecommunicationsMathematicsMathematical optimization

Abstract

fetched live from OpenAlex

Downlink of a multiuser MIMO system is considered, in which the base station (BS) and the user terminals are both equipped with multiple antennas. Efficient transmission schemes based on zero-forcing (ZF) linear receiver processing, eigenmode transmission, and partial channel state information (CSI) at the BS transmitter are proposed. The proposed schemes utilize a handshaking procedure between the BS and the users to select (schedule) a subset of users and determine the precoding matrix at the BS. The advantage of the proposed limited feedback schemes lies in enabling relatively low-complexity user scheduling algorithms and high sum-rate throughput, even for a small pool of users. For large user pools and when the number of antennas at each user terminal is at least equal to the number of antennas at the BS, we show that the proposed scheme is asymptotically optimum.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.462

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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 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

Citations6
Published2010
Admission routes2
Has abstractyes

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