Dynamic spectrum management for cognitive radio: an overview
Bibliographic record
Abstract
Abstract The currently in use spectrum management policies are responsible for the poor utilization of the electromagnetic radio spectrum. By performing dynamic spectrum management (DSM), cognitive radio (CR) has the potential to increase the radio spectrum efficiency significantly and has gained a lot of attention recently. In this paper, we present an overview of the DSM problem in CR. After describing the CR briefly, the DSM is explained. In order to increase the spectrum utilization efficiency, CR tries to share the spectrum with primary users. We discuss two methods for spectrum‐sharing, namely price‐based spectrum‐sharing and opportunistic spectrum‐sharing. After introducing necessary mathematical definitions, the formulation of the DSM problem is presented. We show that the DSM problem is equivalent to a well‐known graph‐coloring problem (GCP) called list‐coloring. Finding the exact solution for this problem is computationally intensive and various approximate algorithms have been proposed to obtain suboptimum solutions. Finally, we discuss two approaches for solving the DSM problem: centralized approach and decentralized approach. Decentralized approach, although has complicated design and may not achieve the global optimum solution, is more suitable for CR due to scalability and lower complexity. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".