Optimal Design of the Spectrum Sensing Parameters in the Overlay Spectrum Sharing
Bibliographic record
Abstract
In this paper, a novel approach is proposed to obtain the optimal operating point of spectrum sensing in overlay spectrum sharing systems. The objective is to maximize the secondary service achievable capacity subject to the primary service collision probability as well as the other system and service constraints. In the related literature the miss detection probability, as the main reason of collision, is often considered to model the impact of spectrum sensing on the achievable ergodic capacity of the secondary service. In this paper, however, we directly consider the collision probability constraint in finding the optimal ergodic capacity instead of considering the miss detection probability. We then propose a framework in which other opportunities which lie in the wireless channel fluctuation and power allocation are also extracted in favor of achieved capacity. In addition to the conventional One-Shot (O-S) scheme, we also propose four novel approaches to solve the optimization problem: Modified-One-Shot (M-O-S) scheme, Multi-Shot (M-S) scheme, Conservative-Modified-One-Shot (C-O-S) scheme, and Restricted-Modified-One-Shot (R-O-S) scheme. Our studies show that the proposed formulation results in a higher secondary service capacity even when compared to the cases with very low miss detection probability. In the proposed schemes in this paper, the main decision parameter is the average (over fading) received interference at the secondary service receiver due to the primary service transmission, I, which can be simply measurable in the secondary transmitter. Extensive numerical studies are conducted to investigate various system aspects. Our studies further suggest that for very low, moderate, and very high values of \(I\) , the proper schemes are C-O-S, M-S, and M-O-S, respectively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".