MétaCan
Menu
Back to cohort
Record W2068355928 · doi:10.1109/lsp.2007.896176

Receive Antenna Selection Based on Union-Bound Minimization Using Convex Optimization

2007· article· en· W2068355928 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Signal Processing Letters · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransmitterMathematical optimizationMIMOAntenna (radio)Selection (genetic algorithm)Convex optimizationComputer scienceOptimization problemComputational complexity theoryMinificationAlgorithmMathematicsUpper and lower boundsInterior point methodRegular polygonTelecommunicationsBeamforming

Abstract

fetched live from OpenAlex

Despite their high spectral efficiencies, multiple-input multiple-output (MIMO) systems suffer from high cost and complexity due to multiple radio frequency chains at both link ends. A possible solution is to select a subset of the available antennas at transmitter and/or receiver based on maximal capacity or minimal error rates. In this letter, we propose a receive antenna selection algorithm based on the minimization of the union bound on the vector error rate. By relaxing the antenna selection variables from discrete to continuous, we arrive at a convex optimization problem. Efficient numerical methods such as interior-point algorithms can be applied to solve this optimization problem with polynomial complexity.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.989

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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