Reducing Handoff Latency for WiMAX Networks Using Mobility Patterns
Bibliographic record
Abstract
In recent years, Worldwide Interoperability for Microwave Access (WiMAX) has become an important technology providing wireless connections for mobile terminals in a wireless Metropolitan Area Network (MAN), due to its large radio range. In a MAN environment, wireless clients always have high mobility; therefore, it is possible that the mobile clients will move away from the service coverage of serving base stations and change their associating base stations. The process of switching between different base stations is known as the handoff process. During the handoff process, the connection between the mobile terminal and the serving base station ceases. The quality of mobile wireless networks is significantly affected by handoff latency and packet loss ratio. In this paper, we propose a fast handoff scheme using mobility patterns for WiMAX networks. Mobility patterns are adopted to predict the next base station and therefore waive unnecessary scans, and the serving base station forwards the data packets received during the handoff process to the target base station for the minimizing of the packet loss ratio. Extensive simulation experiments are conducted to evaluate the performance of the proposed scheme. The results demonstrate that our scheme can shorten the handoff latency.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".