Power efficient multicast for multiple description media in wireless mesh networks
Bibliographic record
Abstract
In this paper, we study multicasting media traffic that uses multiple description coding (MDC) in a wireless mesh network (WMN), where an access point (AP) transmits multiple descriptions to the mobile stations (MSs) through relay stations (RSs). The MSs have different quality of service (QoS) requirements in terms of number of required descriptions, and each RS can forward at most one description. All RSs forwarding the same descriptions form a multicast tree. Our objective is to minimize total transmission power of the RSs, subject to satisfying the QoS requirements of the MSs. We study two problems, building node-disjoint multicast trees and allocating transmission power. The former is to decide which RSs should forward the same description, and the latter is to determine an adequate transmission power level for each RS. An optimization problem is first formulated, and two heuristic schemes are then proposed. The first scheme is a greedy method that iteratively adds new paths to individual multicast trees and assigns transmission power to RSs, and the second one is a simplified version of the first. Numerical results demonstrate that both schemes achieve much lower power consumption compared to a spanning-tree-based scheme that builds the multicast trees one after another, and the power consumption of the first scheme is much lower than the second one at a price of higher complexity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".