Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When incorporating machine-to-machine (M2M) communications into the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) networks, one of the challenges is the traffic overload due to a large number of machine type communication (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 thesis, we first present an analytical model to determine the expected total service time. For the ideal case that the LTE base station (eNodeB) has the information of the number of backlogged users, we determine the optimal value of the ACB factor, which can reduce 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. We also study the scenario where the number of preambles dedicated to M2M traffic is not fixed and investigate whether dynamic resource allocation can reduce the average number of random access opportunities per MTC device. Simulation results show that the fixed resource allocation scheme can achieve as good performance as the dynamic scheme in reducing the number of opportunities and thus dynamic resource allocation is not necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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