Mobility model for heterogeneous wireless networks and its application in common radio resource management
Why this work is in the frame
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Bibliographic record
Abstract
User distribution and mobility behaviour vary based on environment types and characteristics. Heterogeneous wireless networks (HWNs) are deployed to utilise these characteristics and serve users with better quality. For efficient resource management in HWN environment, an understanding of multi-mode user mobility behaviour is paramount. Here, a multi-mode user mobility model is proposed in the context of wireless local area network (WLAN) coverage in the hotspot, overlaid on a macrocell of wireless wide area network (WWAN). An expression for microcell residence time of multi-mode users in HWNs is derived, based on the cell residence time in the constituting WLAN and WWAN. The boundary-crossing probabilities of moving into microcell, moving out of microcell and moving out of macrocell during a call for different types of hotspot topologies are also derived analytically. The numerical results obtained using the analytical expressions for boundary-crossing probability are validated by simulation results. The significance of the proposed mobility model is demonstrated through its application in common radio resource management (CRRM). Numerical results show that the mobility-based CRRM scheme exhibits a lower rate of unnecessary vertical handoffs than that achieved by the ‘WLAN ‘if coverage’ scheme that does not use mobility information for resource management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| 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 it