A scalable overload control algorithm for massive access in machine-to-machine networks
Why this work is in the frame
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Bibliographic record
Abstract
The random access (RA) procedure used in Long Term Evolution-Advanced (LTE-A) suffers from frequent collisions, and thus it is inefficient for machine-to-machine (M2M) networks with massive access. In such networks, network overload problem arises as a performance bottleneck that leads to high resource wastage and severe access delay. Therefore, developing efficient overload control algorithms plays an essential role in future M2M communication. In this paper, we introduce a distributed scalable algorithm that is able to efficiently allocate the available network resources to massive number of devices with bounded delay and reduced overhead. Additionally, the algorithm overcomes the resource wastage problem resulted from RA collisions, and thus, it achieves full resource utilization. Our simulation results show that the proposed algorithm outperforms the existing dynamic access class barring (DACB) algorithm in terms of service time, access delay, and blocking probability.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it