Design of OMC-MAC: An Opportunistic Multi-Channel MAC with QoS Provisioning for Distributed Cognitive Radio Networks
Bibliographic record
Abstract
Dynamic resource availability and lack of central control unit offer many challenges while designing medium access control (MAC) protocol for a distributed cognitive radio network (DCRN). In this paper, we propose a novel MAC design for DCRN which provides an efficient approach to address quality of service (QoS) requirements of delay sensitive applications by defining higher priority to such applications during channel reservation. It also combats other major challenges such as efficient spectrum utilization, multi-channel hidden terminal problem (MHTP) and collision with primary user (PU) due to sensing error at SU. We develop an analytical framework to study the performance of the proposed protocol. We then compare the performance of proposed protocol with those of two existing protocols. Comparison results show that proposed MAC outperforms the existing protocols by providing better throughput and reducing DCRN users' collision probability with PUs in presence of sensing error. The results achieved from the analytical model and validated by simulations show that our simple yet efficient design identifies and fulfils the QoS requirements of delay sensitive applications, achieves excellent spectrum utilization, shows superb robustness in presence of sensing errors and handles MHTP effectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".