Efficient techniques for cooperative spectrum sensing in cognitive radio networks
Why this work is in the frame
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Bibliographic record
Abstract
In this thesis, we focus on two important design aspects of cooperative spectrum sensing (CSS) in cognitive radio networks which are the selection criterion of cooperating secondary users and the fusion technique for combining their local sensing decisions. We propose a novel adaptive user-group assignment algorithm that addresses the problem of sensing accuracy-efficiency trade-off in group-based CSS with heterogeneous cooperating secondary users. The performance of the proposed algorithm is bounded by 4.2% of the optimal solution. Through extensive simulations, we demonstrate that the proposed algorithm can effectively improve the performance of CSS in terms of the opportunistic throughput, sensing overhead and the number of sensing rounds needed to discover an available channel. Considering the different detection performance of cooperating secondary users, we propose a novel reliability-based decision fusion scheme in which a weight is assigned to each secondary user's local decision based on its reliability. Since the knowledge of the local probabilities of detection and false alarm for each secondary detector may not be known in practice, we employ a counting process to estimate those probabilities based on past global and local decisions. We then formulate the problem of minimizing the network probability of sensing error and develop a dual search algorithm, based on a non-linear Lagrangian approach, to solve the formulated problem. Our simulation results show that the dual algorithm converges to the optimal value with zero duality gap using few numbers of iterations. We also show that the probability of error is reduced by 18% and 88% compared to the OR and AND fusion rules, respectively, when the number of secondary users is eight. We then address the practical concern of secondary users reporting correlated local decisions to the fusion center. For this scenario, we formulate the problem of minimizing the network probability of sensing error optimization problem and employ the genetic algorithm to jointly find the optimal K*-out-of-M fusion rule and the optimal local threshold for a certain correlation index. Simulation results show that the network probability of sensing error degrades as the degree of correlation between cooperating secondary users increases. We also study the problem of multiband cooperative joint detection in the presence of sensing errors due to time offset. We derive the aggregate opportunistic throughput and aggregate interference to primary users for multiband cooperative joint detection in the presence of time offset. Our numerical results demonstrate the negative impact of the time offset on the aggregate opportunistic throughput of multiband cooperative joint detection.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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