MétaCan
Menu
Back to cohort
Record W2112417096 · doi:10.1109/icassp.2009.4960102

Outage-based designs for multi-user transceivers

2009· article· en· W2112417096 on OpenAlexaff
Michael Botros Shenouda, Timothy N. Davidson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransceiverQuality of serviceComputer scienceTransmitterChannel state informationTransmitter power outputInterference (communication)Channel (broadcasting)Upper and lower boundsMathematical optimizationAntenna (radio)Base stationSignal-to-noise ratio (imaging)Mean squared errorComputer networkWirelessTelecommunicationsMathematicsStatistics

Abstract

fetched live from OpenAlex

We consider a broadcast channel with multiple antennas at the base station and single-antenna receivers, and we study transceiver design with quality of service (QoS) requirements in the presence of uncertain channel state information (CSI) at the transmitter. Each user's QoS requirement is formulated as an upper bound on the outage probability of the mean square error (MSE), and we demonstrate that these constraints imply bounds on the outage of the received signal-to-interference-plus-noise-ratio. Using this MSE framework, we provide a unified approach to the design of non-linear and linear transceivers that minimize the transmitted power required to satisfy the QoS constraints. We present three conservative design approaches that yield (deterministic) convex and efficiently-solvable design formulations that guarantee the satisfaction of the QoS constraints, and we propose computationally-efficient algorithms that can reduce the level of conservatism in the initial formulations.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.262
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations34
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced MIMO Systems OptimizationFrench-language works237,207