An Enhanced Reservation-Based Medium Access Control with Scheduling and Admission Control for Voice over Wireless Mesh Networks
Bibliographic record
Abstract
Voice over IEEE 802.11 networks is a costeffective solution compared to cellular telephony in small areas and therefore considered among the killer applications of emergent Wireless Mesh Networks (WMNs). However, traditional random medium access control (MAC) protocols used in IEEE 802.11-based WMNs, which are suitable for throughput-sensitive data services, are far from guaranteeing the low delays and the low packet losses required by voice traffic. Therefore, in this paper we propose a new enhanced MAC, called EMDA, combined with a simple scheduling and admission control algorithm, called SAC, that provides a high capacity and quality guarantees for voice over WMNs. EMDA improves significantly the optional Mesh Deterministic Access (MDA) MAC protocol, to provide a much higher voice call capacity thanks to voice packet aggregation and less MDA signaling overhead. EMDA is based on a per-node's radio interface transmission opportunity reservations that are well dimensioned. Moreover, we further improve the deterministic access of EMDA by proposing a jamming scheme that provides more robustness against interfering non-MDA nodes. The proposed SAC algorithm guarantees hard delay constraints and a uniform voice capacity over WMNs regardless of the number of hops to a gateway (i.e., a mesh router connecting WMN to Internet). Simulation results show that our proposed scheme provides a bigger voice capacity that is uniformly distributed over the WMN and a better performance in guaranteeing hard delay constraints and lower packet losses compared to other schemes.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".