Performance evaluation framework for vertical handoff algorithms in heterogeneous networks
Bibliographic record
Abstract
The next generation (4G) wireless network is envisioned as a convergence of different wireless access technologies providing the user with the best anywhere anytime connection and improving the system resource utilization. The integration of wireless local area network (WLAN) hotspots and the third generation (3G) cellular network has recently received much attention. While the 3G-network can provide global coverage with a low data-rate service, the WLAN can provide a high data-rate service within the hotspots. Although increasing the underlay network utilization is expected to increase the user available bandwidth, it may violate the quality-of-service (QoS) requirements of active real-time applications. Hence, achieving seamless handoff between different wireless technologies, known as vertical handoff (VHO), is a major challenge for 4G-system implementation. Several factors, such as application QoS requirements and handoff delay, should be considered to realize an application transparent handoff. We present a novel framework to evaluate the impact of VHO algorithm design on system resource utilization and user perceived QoS. We used this framework to compare the performance of two different VHO algorithms. The results show a very good match between simulation and analytical results. In addition, it clarifies the tradeoff between achieving high resource utilization and satisfying user QoS expectations.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".