Utility-optimal random access: Reduced complexity, fast convergence, and robust performance
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Bibliographic record
Abstract
In this paper, we propose two distributed contention-based medium access control (MAC) algorithms for solving a network utility maximization (NUM) problem in wireless ad hoc networks. Most of the previous NUM-based random access algorithms have one or more of the following performance bottlenecks: (1) extensive signaling among the nodes to achieve semi-distributed implementations, (2) synchronous updates of contention probabilities, (3) small update stepsizes to ensure convergence but with typically slow speed, and (4) supporting a limited range of utility functions under which the NUM is shown to be convex. Our proposed algorithms overcome the bottlenecks in all four aspects. First, only limited amount of message passing among nodes is required. Second, fully asynchronous updates of contention probabilities are allowed. Furthermore, our algorithms are robust to arbitrary large message passing delay and message loss. Third, we do not utilize any stepsize during updates, thus our algorithms can achieve faster convergence. Finally, our proposed algorithms have provable convergence, optimality, and robustness properties under a wider range of utility functions, even if the NUM problem is non-convex. Simulation results show the optimality and fast convergence of our algorithms, performance improvements compared with the subgradient-based MAC, and better efficiency-fairness tradeoff compared with the IEEE 802.11 distributed coordination function.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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