Age of Information Minimization for Opportunistic Channel Access
Bibliographic record
Abstract
This paper investigates how to suppress the Age of Information (AoI) in an opportunistic channel access system, which allows multiple mobile devices to access a base station without central coordination while being aware of instant channel quality. An optimization problem is formulated to minimize the average AoI by optimizing each mobile device’s probability of contending for channel access opportunity and the threshold of offload rate. We derive the exact expression of the average AoI and generate a reformulated optimization problem. Although being non-convex, the reformulated problem is tackled by the following operations. First, we leverage the Dinkelbach method and the block coordinate descent method to convert the reformulated problem into an iterative solving procedure of two non-convex sub-problems, which optimize the contending probability and the threshold of offload rate respectively. Second, for each non-convex sub-problem, we explore the piecewise differential monotonicity for the cost function, and achieve the associated optimal solution by transforming them into standard monotonic optimization problems. Numerical results can verify the effectiveness of the proposed method through the comparison with benchmark methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".