One Bit Feedback for CDF-Based Scheduling with Resource Sharing Constraints
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".