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Record W2584419254 · doi:10.1109/glocom.2016.7841908

Interference Alignment for Heterogeneous Full-Duplex Cellular Networks

2016· article· en· W2584419254 on OpenAlex
Amr El‐Keyi, Halim Yanıkömeroğlu

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 institutionsCarleton University
Fundersnot available
KeywordsMacrocellTelecommunications linkDuplex (building)Base stationComputer scienceInterference (communication)PrecodingInterference alignmentFemtocellMacroComputer networkFemto-Transmission (telecommunications)Electronic engineeringTelecommunicationsMIMOEngineeringBeamforming

Abstract

fetched live from OpenAlex

In this paper, we consider a heterogeneous network composed of a full-duplex macrocell and a half- duplex femtocell. The macro-base station (BS) is equipped with L antennas where each antenna can be utilized either for downlink transmission or uplink reception. On the other hand, the femto-BS is equipped with M antennas that are used for downlink transmission. We assume that each user equipment has N antennas where L>M≥N. We investigate the benefit of using interference alignment for cross-tier and full-duplex interference management and present precoding schemes that are capable of achieving the total degrees of freedom of the network. We show that full-duplex operation of the macro-BS improves the degrees of freedom (DoF) of the system when M2N, otherwise half-duplex operation is optimal. Furthermore, the DoF of the system can be improved by 50\% via full-duplex macro-BS operation when M=N.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.486

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 head0.017
GPT teacher head0.217
Teacher spread0.199 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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