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Record W1971986732 · doi:10.1109/cjece.2013.6601082

Downlink beamforming through relays: imperfect CSI and coordinated transmission

2013· article· en· W1971986732 on OpenAlexaffvenue
Yi Zheng, Steven D. Blostein

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsQueen's University
Fundersnot available
KeywordsBeamformingPrecodingBase stationRelayComputer scienceTelecommunications linkComputer networkTransmission (telecommunications)MIMOTransmitter power outputContext (archaeology)Electronic engineeringTelecommunicationsPower (physics)EngineeringChannel (broadcasting)TransmitterGeography

Abstract

fetched live from OpenAlex

Future wireless systems face challenges in supporting high-rate multimedia streaming with wide coverage area and at low power. Cooperative relaying is investigated in the following context: a single base station with multiple antennas simultaneously transmits different data streams to single-antenna destinations through a set of single-antenna fixed relays, with no direct link transmission between base stations and destinations. Transmission is via space-division multiple access with per-user quality-of-service constraints. The base station performs transmit beamforming (precoding) and relays cooperatively perform distributed beamforming. To address minimum-power source precoding and relay beamforming, the source precoder design with a fixed relay beamformer is first considered. Precoding is also generalized to multiple cooperating base stations. Next, the relay beamformer is optimized for a given source precoder and the process is iterated. The formulation is generalized to account for CSI estimates obtained from pilot symbol training. Simulation results quantify tradeoffs, including numbers of base station antennas and relays, effect of CSI quality on performance, as well as the impact of cooperating base stations.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.386

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 communication0.0000.001
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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 designOther design
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

Citations1
Published2013
Admission routes2
Has abstractyes

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