Cooperative Spectrum Sensing in Cognitive Radio Networks With Noncoherent Transmission
Bibliographic record
Abstract
The problem of decision fusion for cooperative spectrum sensing in cognitive radio (CR) networks is studied when fading channels are present between the CRs and the fusion center (FC). The CRs perform spectrum sensing using energy detection and transmit their binary decisions to the FC for a final decision on the absence or presence of the primary user activity. Considering the limited resources in CR networks, which makes it difficult to acquire the instantaneous channel-state information, noncoherent transmission schemes with on-off keying (OOK) and binary frequency-shift keying (BFSK) are employed to transmit the binary decisions to the FC. For each of the transmission schemes considered, energy- and decoding-based fusion rules are developed first. Then, the detection threshold at the CR nodes and at the FC, the combining weights (in the case of the energy-based fusion rule), and the sensing time are optimized to maximize the achievable secondary throughput of the CR network. Simulation results verify the theoretical analysis. It is also shown that the energy-based fusion rule outperforms the decoding-based fusion rule when the signal-to-noise ratios (SNRs) of the reporting channels are low, whereas the opposite is true for high SNRs. For the simpler energy-based fusion rule, BFSK achieves higher secondary throughput than OOK, at the expense of a larger transmission bandwidth.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".