Network selection strategy for cognitive radios with heterogeneous primary networks
Bibliographic record
Abstract
Dynamic spectrum sharing is a promising approach to reusing the underutilized radio spectrum in a cognitive radio system (CRS). Potential available spectrum for CRS can be from multiple primary radio systems (PRSs) with different spectrum prices, and secondary users (SUs) with heterogenous usage modes, interference punishments and service types. Choosing which primary network to access for SU brings the network selection problem. This scenario generates new opportunities for CRS to improve the system performance by exploiting heterogenous characteristics of PRSs. In this paper, we first use continuous time Markov chain (CTMC) model to describe the network selection problem and derive the performance metrics. Then we intuitively propose Random and Greedy network selection strategies under this framework. Motivated by the drawbacks of those strategies, we categorize the service types of SU into Real-Time Services (RTS) and Non-Real-Time Services (NRTS) and propose an optimal network selection algorithm to maximize performance metrics based on policy iterations. Lastly, the performance of the proposed designs is compared and evaluated through simulation and numerical results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".