Vertical Mobility Management Architectures in Wireless Networks: A Comprehensive Survey and Future Directions
Bibliographic record
Abstract
Mobile users and applications are putting pressure on wireless network operators to improve the seamless handover of devices and services. Strong business competition for subscribers, along with the ever increasing availability of wireless networks will give nomadic and mobile users the opportunity, and systems the power, to make better handover decisions. In this paper, we present a comprehensive review of the literature on mobility management architectures for seamless handover of mobile users in heterogeneous networks. We describe the design rationale for selected architectures, with an in-depth analysis of their main goals, assumptions, and requirements. We also provide directions for further work in this field by highlighting the mandatory requirements and the features of future architectures. We then present a new architecture called Context-Aware Mobility Management System (CAMMS). CAMMS is a new cross-layer, context-aware and interactive approach to seamless handover of users and services. With that proposal, we identified the essential functional entities that must be part of future architectures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".