MétaCan
Menu
Back to cohort
Record W2076526806 · doi:10.1109/vetecf.2010.5594453

Robust DVB-T/H Receiver in Fast Fading Channels

2010· article· en· W2076526806 on OpenAlexaff
Liang Zhang, Zhihong Hong, Louis Thibault

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsFadingDigital Video BroadcastingComputer scienceDiagonalChannel (broadcasting)AlgorithmDVB-TInterference (communication)Multiuser detectionElectronic engineeringOrthogonal frequency-division multiplexingTelecommunicationsMathematicsEngineeringCode division multiple access

Abstract

fetched live from OpenAlex

We previously proposed an iterative decision-directed channel estimation and inter-carrier interference (ICI) cancellation (IDD-ICICan) technique based on estimating the diagonal and off-diagonal vectors of the channel frequency response matrix (CFRM) of a double-selective fading channel. In this paper, we first present some new theoretical analysis that is missing from the previous investigation, including the correlation property within each off-diagonal vectors of the CFRM, and the derivation of the constant scaling relation between different off-diagonal vectors. An enhanced IDD-ICICan (EIDD-ICICan) is then proposed for better performance. At last, we report the simulation performance of a Digital Video Broadcasting (DVB) receiver equipped with the EIDD-ICICan, demonstrating its robust performance even in very fast fading channels. In addition, we show that the superior performance is achieved with well affordable complexity.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.235
Teacher spread0.216 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Communication TechniquesFrench-language works237,207