Optimality of threshold policies for transmission scheduling in correlated fading channels
Why this work is in the frame
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Bibliographic record
Abstract
We consider exploiting perfect channel state information for optimal scheduling for point-to-point data transmission over correlated fading wireless channels, where retransmissions are allowed via the use of a channel-aware ARQ protocol. The objective is to achieve a trade-off between energy and packet loss rate subject to a hard delay constraint. Specifically, the aim of the transmission scheduling problem is to minimize the sum of accumulated transmission costs and a penalty cost on the number of lost packets, subject to the constraint that each batch of a finite number of link layer packets has to be transmitted within a prespecified number of transmission time slots. Using the concept of supermodularity, we prove that under some conditions on the costs, the optimal transmission scheduling policy is threshold in the residual transmission time and the buffer occupancy. These two threshold results substantially reduce the computational complexity required to implement the optimal transmission scheduling policy.
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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 | 0.000 | 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