Cooperative Spectrum Sensing for Cognitive Heterogeneous Networking Using Iterative Gauss-Seidel Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heterogeneous networks with dense deployment of small cells can employ cognitive features to efficiently utilize the available spectrum resources. Spectrum sensing is the key enabler for cognitive radio to detect the unoccupied channels for data transmission. In order to deal with shadowing and multipath fading in sensing channels, cooperative spectrum sensing is designed to increase the accuracy of the sensed signal. In this paper, an optimized local decision rule is implemented for the case that the received data from primary users are possibly correlated due to the sensing channel impairments. Since the prior information is unavailable in the real systems, Neyman-Pearson criterion is used as the cost function. Then, a discrete iterative algorithm based on Gauss-Seidel process is applied to optimize the local cognitive user decision rules under a fixed fusion rule. This method with low complexity can minimize the cost using the golden section search method in a finite number of iterations. ROC curves are depicted using the achieved probability of detection and false alarm by numerical examples to illustrate the efficiency of the proposed algorithm. Simulation results also confirm the superiority of the proposed method compared to the conventional topologies and decision rules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it