Partial mobile data offloading with load balancing in heterogeneous cellular networks using Software-Defined Networking
Bibliographic record
Abstract
The proliferation of mobile services and the explosive growth of data traffic has created new challenges in cellular networks. Mobile data offloading has attracted significant attention, since it has the ability to alleviate cellular burden by using complementary resources and thus, offers better services to end users. In this paper, we introduce intelligence into heterogeneous network management and propose a Software-Defined Networking based module framework, which includes Wi-Fi based partial data offloading and load balancing. Our objective is to make real time decisions for selectively offloading traffic and balancing loads, while taking network conditions and quality of service (QoS) into consideration. The proposed mechanisms are subject to system-level simulations which shows an improvement in load balancing, in terms of equilibrium extent and network stability. We also prove that with the proposed Wi-Fi partial data offloading algorithm, quality of service can be satisfied, while saving a significant amount of cellular resources through smart resource allocation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".