Dynamic access class barring for M2M communications in LTE networks
Bibliographic record
Abstract
When incorporating machine-to-machine (M2M) communications into Long Term Evolution (LTE) networks, one of the challenges is the traffic overload due to a large number of machine type communications (MTC) devices with bursty traffic. One approach to tackle this problem is to use the access class barring (ACB) mechanism to regulate the opportunity of MTC devices to transmit request packets. In this paper, we first present an analytical model to determine the expected total access delay of all the MTC devices. For the ideal case that the LTE base station (eNodeB) knows the number of backlogged users, we determine the optimal value of the ACB factor, which can best reduce the congestion and access delay. For the practical scenario, we propose a heuristic algorithm to adaptively change the ACB factor without the knowledge of the number of backlogged users. Results show that the proposed heuristic algorithm achieves near optimal performance in reducing access delay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".