Gain analysis of cooperative broadcast in two-dimensional wireless networks
Bibliographic record
Abstract
Energy accumulation is an approach to reducing the power consumption of broadcast. In conventional approaches, a node can decode the message if the received power from a single transmission is above a threshold. In contrast, in the cooperative approach based on energy accumulation, a node can decode the message if the sum of the received powers from any set of transmissions exceeds the threshold. An important question is how much energy can be saved in broadcast if energy accumulation is employed. Since employing energy accumulation adds extra design complexities, answering this question can help in deciding whether or not it should be implemented. Previously, it was shown that this saving is limited in linear wireless networks, irrespective of the network size, and the location of the nodes in the network. In this work, however, we show that this saving can increase with the network size in two-dimensional networks. Also, despite the fact that both problems of cooperative and non-cooperative broadcast with minimum energy are NP-hard, we establish a bound on the maximum saving that can be obtained.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".