Integrated approach towards bandwidth aggregation (BAG) in multi-homed devices
Bibliographic record
Abstract
The proliferation of (mobile/portable) devices with the ability to connect to multiple interfaces in parallel (e.g., LTE, Wi Fi, small cells, and even wired LAN in case of a laptop) has led to the emergence of a number of usecases involving bandwidth aggregation - i.e., scenarios where more than one available interface is used to support one application requiring higher bandwidth than each of those individual interfaces can provide. A number of bandwidth aggregation (BAG) approaches have been proposed on different layers of the OSI protocol stack, involving different protocols. The BAG approach in any given protocol layer is best suited only for certain specific scenarios. Further, the use of BAG for a particular application when several other applications are also active on the UE, and the real benefits of employing BAG in such a situation have not yet been studied. This paper proposes an integrated approach towards determining the optimum BAG approach taking into account different parameters such as capabilities of the user, network and the remote peer involved in the communication, network conditions, the nature and QoS requirements of applications that are currently active on the UE, transport and application protocols used, operator's policy and charging requirements, etc. A test bed to evaluate the performance of the approach is also proposed. The objective of this paper is to enable the deployment of BAG in a wide variety of real-world scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".