A Generic Framework for Mobility Modeling and Performance Analysis in Next-Generation Heterogeneous Wireless Networks
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
Integration of different wireless radio cellular technologies is emerging as an effective approach to accommodate the increasing demand of next-generation multimedia-based applications. In such systems user roaming among different technologies, commonly known as vertical handoff, will significantly affect different aspects of network design and planning due to the characteristically wide-ranging diversity in access technologies and supported applications. Hence, the development of new mobility models that accurately depict vertical mobility is crucial for studying different design problems in these heterogeneous systems. This article presents a generic framework for mobility modeling and performance analysis of integrated heterogeneous networks using phase-type distributions. This framework realizes all modeling requirements in next-generation user mobility including accuracy, analytical tractability, and accommodating the correlation between different residence times within different access technologies. Additionally, we present general guidelines to evaluate application performance based on the new mobility models introduced in this article. We show the accuracy of our modeling approach through simulation and analysis given different applications.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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