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Record W2601514283 · doi:10.1109/vtcfall.2016.7881221

Self-Interference Mitigation Using Active Signal Injection for Full-Duplex MIMO-OFDM Systems

2016· article· en· W2601514283 on OpenAlex
Ahmed Masmoudi, Tho Le‐Ngoc

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectronic engineeringComputer scienceSingle antenna interference cancellationMIMOOrthogonal frequency-division multiplexingSIGNAL (programming language)Interference (communication)TelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper presents two self-interference (SI) cancellation methods using active signal injection (ASI) for full-duplex MIMO-OFDM systems. Specifically, the ASI approach considers adding an appropriate cancelling signal to the transmitted signal to reduce the SI at the receiver input to avoid overloading the receiver low-noise amplifier (LNA) and analog-to-digital converter (ADC) while ensuring proper signal detection. In the first method, the cancelling signal uses some reserved subcarriers that are not used for data transmission. In the second method, the constellation points are dynamically extended within the cancellation boundary in order to minimize the SI. The proposed techniques appear to be simple to implement and do not require any change on the receiver structure.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.593

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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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