Analytical modeling of bidirectional multi‐channel IEEE 802.11 MAC protocols
Bibliographic record
Abstract
Abstract This paper presents an analytical approach to model the bi‐directional multi‐channel IEEE 802.11 MAC protocols (Bi‐MCMAC) forad hocnetworks. Extensive simulation work has been done for the performance evaluation of IEEE 802.11 MAC protocols. Since simulation has several limitations, this work is primarily based on the analytical approach. The objective of this paper is to show analytically the performance advantages of Bi‐MCMAC protocol over the classical IEEE 802.11 MAC protocol. The distributed coordination function (DCF) mode of medium access control (MAC) is considered in the modeling. Two different channel scheduling strategies, namely,random channel selectionandfastest channel first selectionstrategy are also presented in the presence of multiple channels with different transmission rates. M/G/1 queue is used to model the protocols, and stochastic reward nets (SRNs) are employed as a modeling technique as it readily captures the synchronization between events in the DCF mode of access. The average system throughput, mean delay, and server utilization of each MAC protocol are evaluated using the SRN formalism. We also validate our analytical model by comparison with simulation results. The results obtained through the analytical modeling approach illustrate the performance advantages of Bi‐MCMAC protocols with the fastest channel first scheduling strategy over the classical IEEE 802.11 protocol for TCP traffic in wirelessad hocnetworks. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".