An Enhanced Reservation Based Medium Access Control for Voice over Wireless Mesh Networks
Bibliographic record
Abstract
Voice over IEEE 802.11 becomes much more cost effective when deployed over IEEE 802.11-based Wireless Mesh Network (WMN) due to the license-free backhaul wireless links. However, medium access control (MAC) based on IEEE 802.11 traditional random backoff protocols that are suitable for throughput-sensitive data services cannot satisfy the performance of voice services. Therefore, in this paper we propose an improved MAC based on medium reservations, called EMDA. EMDA improves significantly the optional Mesh Deterministic Access (MDA) MAC, to provide a much higher voice call capacity thanks to voice packet aggregation feasibility and much less MDA signaling overhead. EMDA is based on a per-node reservation of a block of contiguous transmission opportunities that are properly dimensioned. Moreover, we further improve the near-deterministic access of EMDA by using short jamming periods just before these reservations. Extensive simulation results show that our proposed MAC enlarges the voice capacity and uniformly distribute it over the WMN. It also provide a better performance in guaranteeing hard delay constraints, lower jitter and lower packet losses compared to other MAC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".