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Record W2006002274 · doi:10.1002/ett.2620

SNR‐dependent radius control sphere detection for MIMO systems and relay networks

2013· article· en· W2006002274 on OpenAlex
Shuangshuang Han, Chintha Tellambura, Tao Cui

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

VenueTransactions on Emerging Telecommunications Technologies · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMODecoding methodsAlgorithmHeuristicComputer scienceComputational complexity theorySpatial multiplexingChannel (broadcasting)RADIUSRange (aeronautics)MathematicsTopology (electrical circuits)Mathematical optimizationTelecommunicationsEngineeringComputer network

Abstract

fetched live from OpenAlex

Abstract A new sphere decoder algorithm for uncoded spatial multiplexing multiple‐input multiple‐output (MIMO) systems is proposed. It overcomes the drawbacks of traditional sphere decoders: variable complexity and high complexity in low signal‐to‐noise ratios (SNRs). Its main novelty lies in scaling the search radius by a heuristic SNR‐dependent factor. This new SNR‐dependent radius control sphere decoder offers near maximum likelihood performance over the entire range of SNRs, while keeping its complexity roughly constant. This algorithm also incorporates channel ordering to save complexity. To quantify the variability of complexity, the normalised variance of the complexity is evaluated. This algorithm is also extended for joint iterative detection and decoding in coded MIMO systems and for MIMO‐relay networks. Simulation results and theoretical analysis demonstrate the benefits of the proposed algorithm.Copyright © 2013 John Wiley & Sons, Ltd.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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