Haar Compression for Efficient CQI Feedback Signaling in 3GPP LTE Systems
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it