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Record W2152262955 · doi:10.1109/wcnc.2008.150

Haar Compression for Efficient CQI Feedback Signaling in 3GPP LTE Systems

2008· article· en· W2152262955 on OpenAlex

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
TopicAdvanced Wireless Network Optimization
Canadian institutionsInterDigital (Canada)
Fundersnot available
KeywordsComputer scienceTelecommunications linkScheduling (production processes)Computer networkOverhead (engineering)Real-time computingThroughputBase stationChannel (broadcasting)WirelessEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Frequency selective scheduling is an attractive feature in the 3GPP LTE system that allows optimum usage of the allocated spectrum. In order to support frequency selective scheduling in the downlink, the mobile user needs to feedback channel quality indication (CQI) of the downlink channel to the base station. Several CQI feedback scheme have been proposed for 3GPP LTE systems. We propose to apply Haar compression to distributed subband groups to reduce the CQI feedback overhead. The simulation results indicate that the distributed- Haar scheme achieves the best trade-off between the throughput performance and overhead reduction compared with other CQI feedback schemes. We also observe that the sensitivity in sector throughput performance to user mobility is approximately the same for all feedback methods considered in the paper.

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 score0.798
Threshold uncertainty score0.471

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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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