Self-Organizing Dynamic Spectrum Management for Cognitive Radio Networks
Bibliographic record
Abstract
Dynamic spectrum management (DSM) is one of the key problems in the design of cognitive radio (CR) networks. It is a time-varying and location-dependent optimization problem, equivalent to the well-known graph-colouring problem in graph theory. This problem is known to be N-Phard and computationally challenging to solve. Accordingly, finding the exact solution for the DSM optimization problem is typically not practical. In this paper, we introduce a novel self-organizing DSM scheme, which solves the DSM problem in a decentralized manner. The use of self-organization to address the DSM problem offers several benefits: decentralization and scalability of the network behaviour, computational simplicity, cost-effectiveness and bandwidth conservation. In the paper, we address the underlying principles involved in the design and implementation of the self-organizing DSM as well as a software testbed for demonstrating this novel approach. Experimental results are presented to justify this new approach.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".