MG-Local: a multivariable control framework for optimal wireless resource management
Bibliographic record
Abstract
Competition for finite resources causes severe congestion and collisions in wireless networks. Without effective management, the network can become unstable, and users may experience very long delay, significant packet loss and poor throughput. In this paper, we propose a multivariable globalized-local (MG-Local) framework of resource management to find a balance between fair allocation and efficient utilization. This framework uses adaptive multivariable control to improve control effectiveness. Our design combines the advantages of both global and local optimization methods, and drives the system toward a global optimum by intelligently exploiting local information, without message passing. We demonstrate the effectiveness of this generic resource-management framework by applying it at the medium access control layer, which is the major performance bottleneck in wireless network [1]. Our experimental results show that our method significantly outperforms four other approaches in terms of throughput, packet loss rate, delay, and fairness.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".