Adaptive Assignment of Heterogeneous Users for Group-Based Cooperative Spectrum Sensing
Bibliographic record
Abstract
In this paper, we consider a multichannel cognitive radio network, where cooperative secondary users have heterogeneous sensing ability in terms of their sensing accuracy. We employ a group-based cooperative spectrum sensing (CSS) scheme in which cooperating secondary users are grouped such that different groups are responsible for sensing different channels. In this group-based CSS scheme, channels sharing the same cooperating users are scheduled to sense in different sensing rounds. In this work, we propose adaptively assigning the heterogeneous cooperating secondary users to different groups to maximize the throughput efficiency while maintaining a predefined sensing accuracy. To this end, we analytically derive a closed-form expression for the throughput efficiency in terms of the average opportunistic throughput and average sensing overhead. We also formulate the throughput efficiency maximization problem for heterogeneous secondary users as a nonlinear binary programming problem, which is computationally intractable. We then propose three efficient adaptive assignment heuristics that perform the assignment of users to groups and the assignment of those groups to the sensing rounds such that the throughput efficiency is maximized. Simulation results demonstrate that our proposed assignment heuristics can achieve near optimal performance with low computational complexity and can also improve the throughput efficiency significantly compared to the existing nonadaptive assignment and sequential CSS schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".