Stochastic Delay Guarantees for Devices With Dual Connectivity
Bibliographic record
Abstract
Dual connectivity (DC) is a feature that allows dual-wireless interface devices to concurrently utilize radio resources from two different wireless network technologies. The aggregate data rate achievable using DC is expected to enhance application performance and user experience if the radio resources of contributing wireless networks are efficiently allocated. Moreover, with the current 5G deployment stage, DC is envisioned as a promising solution to address the 5G coverage holes using the existing 4G long-term evolution (LTE) infrastructure. This article presents an approach to provide statistical delay guarantees for delay-sensitive applications running on devices with DC. We propose a stochastic delay-based DC analytical model using the effective bandwidth concept. The model is applied to two case studies, namely, LTE-WiFi connectivity (licensed with nonlicensed) and 5G-LTE connectivity (intergeneration). The proposed model is used as a tool for effective resource allocation by obtaining the optimal uplink traffic share for each network that minimizes the delay violation probability or data transmission cost. Furthermore, using the analytical model, an algorithm for node admission control (NAC) is developed for DC networks. Our simulation results demonstrate that the proposed model and the NAC algorithm can efficiently allocate resources with stochastic delay guarantees for DC networks.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".