A performance comparison of cognitive versus traditional radio networks
Bibliographic record
Abstract
Although the main objectives of dynamic spectrum access and cognitive radios are to solve the problem of spectrum scarcity and usage inefficiency, we show in this paper that such capabilities can also be used to improve other performance metrics in wireless networks. We compare the performance of a Cognitive Radio (CR) network using nodes that are able to sense the spectrum and change their operating frequency in case of link failure with a Traditional Radio (TR) with static channel access and no frequency switching capability. The improvement that can be obtained by substituting TR to CR technology is evaluated for the blocking probability, utilization and system occupancy performance metrics based on a Two-state Markov chain channel model as a function of the number of users, number of channels, arrival rate, restoration time and channel occupancy. We show that there is a strong interaction between these parameters and that CR technology performs better in slowly time-varying channels while TR technology is more appropriate in fast changing channels.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".