Transmission scheduling in a multi‐channel wireless network with bidirectional relaying links
Bibliographic record
Abstract
Abstract Using network coding in a wireless network can potentially improve the network throughput. On the other hand, it increases the complexity of resource allocations as the quality of one transmission is affected by the link conditions of the transmitter to multiple receivers. In this work, we study time slot scheduling and channel allocations jointly for a network with bidirectional relaying links, where the two end nodes of each link can exchange data through a relay node. Two scenarios are considered when the relay node forwards packets to the end nodes. In the first scenario, the relay node always forwards network‐coded packets to both end nodes simultaneously; in the second scenario, the relay node opportunistically uses network coding for two‐way relaying and traditional one‐way relaying. For each scenario, an optimization problem is first formulated for maximizing the total network throughput. The optimum scheduling is not causal because it requires future information of channel conditions. We then propose heuristic scheduling schemes. The slot‐based scheduling maximizes the total transmission rate of all the nodes at each time slot, and the node‐based scheduling schedules transmissions based on achievable transmission rates of individual nodes at different channels. The node‐based one has lower complexity than the slot‐based one. Our results indicate that although the node‐based scheduling achieves slightly lower throughput than the slot‐based one, both the proposed scheduling schemes are very effective in the sense that the difference between their throughput and the optimum scheduling is relatively small in different network settings. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".