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Record W1993714507 · doi:10.1109/lwc.2014.2345751

Self-Interference Pricing-Based MIMO Full-Duplex Precoding

2014· article· en· W1993714507 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 Wireless Communications Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrecodingZero-forcing precodingComputer scienceMIMOInterference (communication)TransmitterElectronic engineeringDuplex (building)WirelessSpectral efficiencyChannel (broadcasting)AlgorithmTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Precoding designs for bidirectional full-duplex (FD) systems have been proposed as potential ways to suppress the effects of self-interference and improve the spectral efficiency of wireless systems. This letter proposes the self-interference pricing based full-duplex precoding (FDP-SIP) algorithm that applies pricing-based precoding at the transmitter to suppress the self-interference prior to the receiver low-noise amplifier (LNA) and analog-to-digital converter (ADC) to avoid overloading while ensuring the receiver linearity. The proposed FDP-SIP algorithm can be implemented without the need of active cancellation at the receiver. Simulation results demonstrate the effectiveness of the proposed FDP-SIP algorithm using both channel models and measured data. In particular, based on the measured data, FDP-SIP algorithm provides the sum-rate nearly 1.8 times that of an optimized half-duplex (HD).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0040.000
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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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