A Queueing Framework for Channel Allocation Protocol in Multi-user Multi-channel Cognitive Radio Network
Bibliographic record
Abstract
In order to optimize the efficiency of radio resource utilization in the cognitive radio network (CRN), the channel allocation protocol plays a crucial role. However, how to build a general and adaptable framework for the design and evaluation of the protocol, especially in a complex context, such as multiuser multi-channel CRN, is still a open issue. A channel allocation framework based on queueing theory is introduced in this paper. A mechanism with flexible and configurable features, namely distribution probability matrix, is applied to implement and evaluate channel allocation protocols. This framework can provide various comprehensive performance evaluations, such as average queue length, throughput and delay, to carry out protocol evaluation. Moreover, the performance metrics of all the users are obtained independently and simultaneously. Using this framework, a modified maximum rate protocol, namely maximum throughput protocol, is implemented and comprehensive performance evaluation compared to the maximum rate protocol is carried out. The convenience and effectiveness of this channel allocation framework is revealed by the modeling process and numerical results.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".