A Reliable Low-Overhead MAC Protocol for Multi-Channel Wireless Mesh Networks
Bibliographic record
Abstract
This paper proposes a multi-channel medium access control (MAC) protocol for wireless mesh networks (WMNs) by using busy tones to prevent data packet collisions at data channels. Multi-channel MAC schemes can achieve higher network throughput than single channel MAC schemes in multi- hop wireless networks. It is especially appealing to exploit multiple channels in WMNs which has high capacity requirement to support backbone multimedia applications. Most previously proposed MAC protocols make use of the RTS/CTS mechanism to deal with data packet collisions caused by exposed/hidden terminal problems in multi-hop environment. However, when multiple channels are used for data transmissions, the traditional RTS/CTS mechanism can no longer handle the exposed/hidden terminal successfully. By investigating the special features of WMN architecture, we apply the busy tone solution into the medium access control mechanism for WMNs, in which mesh nodes have no limit on power consumption. In this paper, we clearly presented the idea and operation of our proposed multichannel MAC protocol for WMNs. Comprehensive simulations are conducted to investigate the effects of various factors on the system performance. Also, the performance of our proposed mechanism is compared with that of previous RTS/CTS-based MAC protocols.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".