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Record W1529790017 · doi:10.1109/pimrc.2005.1651832

Efficient Link Layer Transmission Strategy for MIMO Wireless Systems

2006· article· en· W1529790017 on OpenAlexaff
Wessam Ajib, David Haccoun, Jean‐François Frigon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMIMOComputer scienceSpatial multiplexingTransmission (telecommunications)TransmitterFrame (networking)Channel state informationChannel (broadcasting)Multiplexing3G MIMOContext (archaeology)Antenna (radio)WirelessMulti-user MIMOElectronic engineeringComputer networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper investigates link layer data units (frames) transmission strategies for MIMO wireless systems using spatial multiplexing. A new effective transmission strategy is proposed in this paper in order to decrease the frame error rate by making use of the multi-channel transmission characteristics provided in MIMO systems. The main idea is to select, in the context of a V-BLAST transmitter, between transmitting each frame, where a frame corresponds to an error correcting code word, from one antenna or from multiple antennas according to the channel state. Limited binary feedback information allows the transmitter to select the appropriate frame transmission policy. Analytical studies and simulations provided in this paper determine the optimal selection criterion and highlight the gains obtained by the proposed transmission strategy. This paper confirms that always transmitting each frame from multiple antennas gives quasi-optimal performances

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 score0.983
Threshold uncertainty score0.503

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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