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Record W2001904840 · doi:10.1109/spawc.2014.6941886

Widely linear Interference Alignment precoding

2014· article· en· W2001904840 on OpenAlexaff
Ahmed Medra, Timothy N. Davidson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInterference alignmentPrecodingZero-forcing precodingGrassmannianComputer scienceConstellationInterference (communication)Offset (computer science)Topology (electrical circuits)Channel (broadcasting)Degrees of freedom (physics and chemistry)AlgorithmMIMOMathematicsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Interference Alignment is an intriguing approach to the development of interference management schemes for wireless networks. Even for networks of single antenna nodes with time-invariant channels, several approaches to interference alignment have been proposed. Those schemes construct transmitted signals that are not circularly symmetric by using symbols from real-valued constellations. In this paper we develop an alternative scheme that employs widely linear precoding of symbols from conventional complex-valued constellations at the transmitters, and a periodic conjugation operation at the receivers. Like the existing schemes, the proposed scheme achieves the degrees of freedom of the channel, and it adds the convenience of enabling the use of conventional constellations. To tackle the difficult problem of optimizing the sum rate under the interference alignment constraint, we develop an efficient algorithm on the Grassmannian manifold, and we demonstrate that this algorithm can reduce the power offset and hence provide significant increases in the sum rate at low to moderate SNRs.

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: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.273

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 head0.222
Teacher spread0.209 · 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
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

Citations3
Published2014
Admission routes1
Has abstractyes

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