Power allocation and transmission scheduling for a network with bidirectional relaying links
Bibliographic record
Abstract
Abstract In this paper, we study packet transmission scheduling for a network with bidirectional relaying links, where the relay station can use network coding to combine packets to multiple receivers and opportunistically decide the number of packets to be combined in each transmission. Two cases are considered, depending on whether nodes are allowed to overhear transmissions of each other. A constrained Markov decision process is first formulated with an objective to minimize the average delay of packet transmissions, subject to the maximum and average transmission power limits of the relay node. The complexity for solving the constrained Markov decision process (MDP) is prohibitively high, although the computational complexity for the no‐overhearing case can be greatly reduced. Heuristic schemes are then proposed, one applies to the general case, and another applies to only the no‐overhearing case. Numerical results demonstrate that the heuristic schemes can achieve close‐to‐optimum average packet transmission delay, and furthermore, the second scheme achieves lower maximum delay while keeping the same average packet transmission delay and relay node power consumption as the first one. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".