Spectrum sensing in cognitive radio networks: the cooperation‐processing tradeoff
Bibliographic record
Abstract
Abstract Opportunistic unlicensed access to the (temporarily) unused frequency bands across the licensed radio spectrum is currently being investigated as a means to mitigate the spectrum scarcity. Such opportunistic access calls for the implementation of safeguards so that the ongoing licensed operations are not interfered with. Among different candidates, sensing‐based access, where thesecondary(unlicensed) users transmit if they sense theprimary(licensed) band to be free, is particularly appealing due to its low deployment cost and its compatibility with legacy primary systems. Incorporatingspectral awarenessfunctionality into the radio transceivers is a major step towards the realization of thecognitive radios. In this paper performance of spectrum‐sensing cognitive radios is studied under channel fading. In particular, it is shown that due to the uncertainty resulting from fading, local signal processing alone may be inadequate to meet the performance requirements. To remedy this issue, cooperation among secondary users is proposed and studied in this paper. Moreover, we characterize and study a tradeoff between local processing and cooperation, which should be balanced in order to maximize the spectrum utilization. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".