Hierarchical and Adaptive Spectrum Sensing in Cognitive Radio Based Multi-Hop Cellular Networks
Bibliographic record
Abstract
In a spectrum overlay system, the secondary users (SUs) with cognitive radio capability need to detect the presence of primary users promptly and reliably in order to prevent excessive interference. Likewise, to make full use of the available spectra, such systems have to attain low false alarm probability. Having a high detection probability while maintaining a low false alarm probability is challenging as these are conflicting goals; therefore, an appropriate tradeoff needs to be determined. In this paper we propose a "Hierarchical and Adaptive Spectrum Sensing (HASS)" solution to multi-hop cellular networks, which is based on cooperative sensing and soften hard detection fusion mechanisms with one-bit overhead. SUs that are able to make local detection decisions either directly report their one-bit decisions to the cognitive radio base station or they relay their decisions to other favorable SUs based on the states of their reporting channels. If SUs can not make detection decisions, they relay the observed signals to other favorable SUs for further processing. Simulation results show HASS solution improves spectrum sensing performance in multi-hop cellular networks.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".