Performance analysis of joint power control, rate adaptation, and channel selection strategies for Cognitive Radio Networks
Bibliographic record
Abstract
This article proposes a joint power control, rate adaptation and channel selection strategy for Cognitive Radio Networks (CRNs) operating over vacant TV bands, also known as TV Bands Devices (TVBDs). To exploit the abundance of vacant channels available at the disposal of these devices, we combine the well-studied power and rate adaptation strategies with adaptive channel selection. The combined strategy maximizes the achievable throughput while guaranteeing a desired level of performance, i.e., error and outage probabilities. It also reduces the chances of causing or being subject to harmful interference to/from other co-channel users. Furthermore, to waive the processing complexity resulting from the frequent channel switching, we propose an alternative strategy that switches the operating channel only when the operating channel fails to support the least supportable data rate. For the two strategies, we derive closed form expressions for the average data rates and transmission powers. The accuracy of the derived results as well as the performance gains achieved are verified using intensive numerical and simulations results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".