Intelligent hybrid cooperative spectrum sensing: A multi-stage decision fusion approach
Bibliographic record
Abstract
Cooperative spectrum sensing is a powerful sensing approach which is based on sharing information about channel activities among secondary users (SUs). Cooperative spectrum sensing aims to overcome hidden node problem, shadowing and fading problems, it also enhances sensing accuracy. However, sensing accuracy may degrade due to various reasons: if environmental properties are poor or intra-node characteristics are continuously altering. Thus, this paper proposes a novel approach of a multi-stage hybrid cooperative spectrum sensing model. The first stage, integrates a fuzzy logic system for local fusion center, whereby SU-mobility and its environmental properties, and its neighbors' environmental properties are included in local sensing decision process. Second stage proposes a neural network, based on backpropagation learning algorithm, for global fusion center. All SUs transmit their sensing information, local decision, and their intra-node characteristics to be augmented for an optimized global sensing decision. Neural network is trained based on a real-world measured power dataset. Extensive simulations on the proposed multi-stage model were performed. The results showed high robustness against instantaneous changes in SU-mobility levels and good detection performance at very low signal-to-noise ratio (SNR) levels. The proposed prediction model outperformed the state-of-the-art work with high detection accuracy at very poor environmental conditions and at different speeds levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".