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Record W2785549194 · doi:10.1109/pimrc.2017.8292719

Interference cancellation in full-duplex multicell networks

2017· article· en· W2785549194 on OpenAlexaff
Huan Wu, Eddy Hum, Wanyi Shiu, James Gary Griffiths

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsSingle antenna interference cancellationComputer scienceBase stationTransceiverWireless networkWirelessScheduling (production processes)Radio resource managementInterference (communication)Electronic engineeringComputer networkReal-time computingTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

The advent and advancement of full-duplex (FD) technology in radio transceivers is expected to lead to full-duplex enabled multi-cell (FD-MC) networks in next generation wireless communication systems. Additional interferences between FD nodes and devices are the main challenges for the deployment of such networks. As in an early deployment scenario where the base stations (BS) operate in FD mode while the user equipments (UE) remain in the half-duplex (HD) mode, the self-interference (SI) within the FD-BS, the mutual interference (MI) between the FD-BS's and between the HD-UE's paired for FD scheduling are the major issues that could jeopardize the capitalization of FD system gains if they are not well addressed. In this paper, we present a system architecture for multi-stage cancellation of SI and joint cancellation of MI and residual SI in an FD-MC network. Multiple orthogonal pilots and their derived forms are utilized for channel estimation during a common training period. System-level delay calibration necessary for SI and MI channel estimation and cancellation is also provided.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.468

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 science0.0010.000
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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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