Improved VoIP capacity in mobile WiMAX systems using persistent resource allocation
Bibliographic record
Abstract
Efficient support of voice traffic has always been one of the key metrics for evaluating and selecting radio access technologies. Even for next-generation broadband wireless technologies primarily focused on the mobile Internet, special handling of VoIP traffic in the physical and MAC layers is required to maximize voice capacity. While Mobile WiMAX Release 1.0 and 802.16e have all the key features necessary to support mobile VoIP traffic, special attention is given in Mobile WiMAX Release 1.5 and 802.16REV2 to further optimizing VoIP capacity through reduction in the MAC layer overhead associated with signaling messages. This article focuses on features and solutions used in Mobile WiMAX and the 802.16 standard to support voice traffic and the expected performance in Release 1.0/802.16e-2005, as well as gains from optimization concepts such as persistent allocation added in Release 1.5/802.16REV2. In each case, MAP overhead reduction and the projected improvements in VoIP capacity are presented using typical industry accepted models and assumptions. The results show about 15 percent increase in bidirectional VoIP capacity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".