Joint Transmission Power Optimization and Connectivity Control in Asymmetric Networks
Bibliographic record
Abstract
In this paper, the problem of transmission power optimization and connectivity control over asymmetric networks represented by weighted directed graphs (digraphs) is investigated using a centralized approach. The notion of generalized algebraic connectivity (GAC) introduced in the literature recently as a measure of connectivity in weighted digraphs is formulated as an implicit function of the network's transmission power vector. An optimization problem is then presented to minimize the total transmission power of the network while satisfying certain constraints on the GAC and transmission power. The interior point method is used to transform this constrained optimization problem into a sequential unconstrained optimization problem. Each subproblem is then solved numerically using the subgradient method with backtracking line search. Even though the GAC is a non-convex and non-differentiable continuous function of the network's transmission power vector, using the aforementioned methods the optimization problem gradually becomes convex as the number of iterations increases. Asymptotic convergence of the proposed algorithm to the global minimum of the original optimization problem is demonstrated analytically. The effectiveness of the algorithm is verified by simulations.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".