One Bit Feedback for CDF-Based Scheduling with Resource Sharing Constraints
Why this work is in the frame
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Bibliographic record
Abstract
Cumulative distribution function (CDF)-based scheduling (CS) is known to be effective in meeting the different channel access ratio (CAR) requirements of users in a multi-user wireless system. In this paper, we propose a one-bit-feedback scheme for CS (OBCS) to reduce the feedback overhead from users in a cell. In OBCS, each user sets its individual threshold to decide whether to send one-bit feedback to the base station (BS). The BS randomly generates numbers for all users based on their feedback behavior and selects a user who is assigned with the largest value. We further propose OBCS with reduced complexity, OBCS-RC, which employs a universal threshold for all users, and relieves the BS to generate random numbers only for the users who have sent feedback. Both OBCS and OBCS-RC inherit the properties of CS in meeting diverse CAR requirements of users in arbitrary fading channels. Extensive analytical and simulation results indicate that simply setting the OBCS-RC threshold to 0.1 is adequate for good throughput performance compared to OBCS with the optimal threshold for each user. Although OBCS and OBCS-RC induce a throughput loss due to the reduced feedback overhead, their throughput still grows in a double-logarithmic manner as CS in Nakagami-m channels when the number of users increases to infinity.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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