Cooperative Spectrum Sensing for Wideband Cognitive OFDM Radio Networks
Bibliographic record
Abstract
One of the fundamental requirements of cognitive radio networks is to reliably detect the presence of licensed primary users. Therefore, spectrum sensing should be performed prior to allowing unlicensed users to access the vacant licensed bands. Multiple secondary users can cooperate to increase the reliability of spectrum sensing. Previous work on wideband spectrum sensing showed that multiband joint detection, which jointly detects the signal energy over multiple frequency bands, is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. In this paper, we investigate the integration of basic wideband spectrum sensing with both data (soft) and decision (hard) fusion techniques to improve the performance in the presence of multiple secondary users. We formulate the optimization problem for the multiband joint detection when cooperation is used for both hard and soft decision approaches. Numerical results show the significant improvement in the performance, in terms of the aggregate opportunistic throughput and false alarm probability, achieved by using cooperative sensing. Also, better performance was achieved when the data fusion approach was used.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".