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Cancelling Adjacent Channel Interference for In-Band Full-Duplex Communications

2024· article· en· W4401164255 on OpenAlexaff
Zhihong Hong, Yiyan Wu, Wei Li, Liang Zhang, Sung-Ik Park, Sungjun Ahn, Namho Hur, Eneko Iradier, Jon Montalbán, Pablo Angueira

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsInterference (communication)Adjacent-channel interferenceDuplex (building)Channel (broadcasting)Computer scienceCo-channel interferenceTelecommunicationsElectronic engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

When operating in the in-band full-duplex (IBFD) mode, wireless in-band distribution link (IDL) is a spectral efficient and cost-effective backhaul technology for Advanced Television Systems Committee (ATSC) 3.0 in a single-frequency network (SFN), since IDL shares the same frequency band allocated for the traditional broadcast services. Therefore, it is desirable to use high-order modulation, e.g., 1024QAM, to achieve a very high data rate for the IDL, such that more bandwidth can be reserved for broadcast service. This presents stringent requirements for the self-interference (SI) cancellation (SIC) at the IDL receiver where the leakage from the co-located transmitter as self-interference seriously corrupts the received signal. Such SI signal is nonlinearly distorted by the high power amplifier (HPA). Moreover, the transmitters broadcast multiple channels at the same time. Therefore, the SI signal also contains leakage from adjacent channels, which is also nonlinear in nature. Accurately cancelling the SI signal with HPA-induced nonlinear distortion and adjacent channel interference (ACI) is considered in this paper. Simulation results demonstrate that under the presence of nonlinear distortion and ACI, linear SIC fails to achieve satisfactory performance, while the previously proposed iterative successive nonlinear SI cancellation (ISNSIC) can effectively cancel the SI with both nonlinear distortion and ACI.

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 score0.460
Threshold uncertainty score0.619

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 head0.046
GPT teacher head0.283
Teacher spread0.237 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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