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Record W2065588728 · doi:10.1109/glocom.2013.6831682

Self-interference pricing for Full-Duplex MIMO systems

2013· article· en· W2065588728 on OpenAlexaff
Sean Huberman, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsMcGill University
Fundersnot available
KeywordsInterference (communication)Computer scienceMIMOTransmitterSpectral efficiencyCo-channel interferenceAdjacent-channel interferenceWirelessChannel (broadcasting)TelecommunicationsElectronic engineeringZero-forcing precodingCommunications systemSIGNAL (programming language)Duplex (building)Multi-user MIMOEngineeringPrecoding

Abstract

fetched live from OpenAlex

Full-Duplex (FD) wireless communication systems have recently been proposed as a potential technique for increasing the spectral efficiency of wireless systems. FD communication systems suffer from the self-interference generated from one's own transmitter to one's own receiver in the same time and frequency slot. The self-interference signal is typically significantly larger than the received signal. The majority of existing work focuses on the suppression of self-interference without focusing on the effect on the forward channel. This paper presents a self-interference pricing based approach to suppress the self-interference without overly penalizing the forward channel. Simulation results show that even in the case of high self-interference, the proposed approach can provide significant data-rate improvements.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
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)0.0020.000

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations14
Published2013
Admission routes1
Has abstractyes

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