Reducing Traffic Congestion for Machine to Machine Type Communication Over 4G-LTE Network by Decreasing Total Bytes Transmitted
Bibliographic record
Abstract
In the near future, with the development of Machine to Machine (M2M) communication service providers may see a spike in traffic degrading the quality of service (QoS). With the addition of several M2M devices, it is expected to create conditions for overload in the Radio Access Network (RAN) and Core Network in 3GPP LTE networks. There are many studies that examine various characteristics of M2M communication devices including protecting the physical devices, authentication methods, congestion controls, privacy protection and many others. However, congestion will be a persistent problem with the increased devices and is the focus of this paper. There is research on the methods to control congestion, though this paper is considering increasing availability through reducing total bytes transmitted and thus avoiding or reducing overload and congestion in LTE network. In this paper, we have proposed and tested various optimizing mechanisms for reducing the signalling traffic and bandwidth utilization, thus decreasing the overload in the LTE architecture.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".