Minimum-energy multicast in wireless ad hoc networks with adaptive antennas: MILP formulations and heuristic algorithms
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Bibliographic record
Abstract
In this paper, we consider wireless ad hoc networks that use adaptive antennas and have limited energy resources. To explore the advantages of power saving offered by the use of adaptive antennas, we consider the case of source initiated multicast traffic. We present a constraint formulation for the MEM (Minimum-Energy Multicast) problem in terms of MILP (Mixed Integer Linear Programming) for wireless ad hoc networks. An optimal solution to the MEM problem using our MILP model can always be obtained in a timely manner for moderately sized networks. In addition to the theoretical effort, we also present two polynomial-time heuristic algorithms called RB-MIDP and D-MIDP to handle larger networks for which the MILP model may not be computationally efficient. The experimental results show that our algorithms compare well with other proposals discussed in this paper.
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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