Supporting Differentiated Service in Cognitive Radio Wireless Mesh Networks
Bibliographic record
Abstract
The MAC layer protocols utilizing enhanced distributed channel access (EDCA) in the recently published IEEE 802.11-2007 are able to provide differentiated QoS for different traffic types in wireless networks through varying the Arbitration Inter-Frame Spaces (AIFS) and contention window sizes. However, the performance of high priority traffic can be seriously degraded in the presence of strong noise over the wireless channels. The noise problem is further aggravated in wireless mesh networks when packets traverse multiple-hops from source to destination. The noise problem can be mitigated by distributing network traffic across multiple vacant channels to reduce the node density per transmission channel. Although multiple non-overlapped channels exist in the 2.4GHz and 5GHz spectrum, most IEEE 802.11-based multi-hop ad hoc networks today use only a single channel at anytime. As a result, these networks cannot fully exploit the aggregate bandwidth available in the radio spectrum provisioned by the standards. In this paper, we propose the Power-Controlled Rate-Adaptive MAC (CPCRA) protocol for single transceiver based Cognitive Radio Networks (CRNs) which combines adaptive modulation and coding with dynamic spectrum access. Simulation results demonstrate that CPCRA can achieve better performance in terms of lower delay and higher throughput.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".