Resource Allocation in Traditional and Cooperative Cognitive Radio Networks
Bibliographic record
Abstract
Cognitive radio (CR) is a promising technique to improve spectrum efficiency for wireless communications. This thesis focuses on the resource allocation in two kinds of CR networks (CRNs), traditional CRNs (TCRNs) and cooperative CRNs (CCRNs). In TCRNs, CR sources and destinations communicate directly. By exploring the heterogeneity among CRs, a prioritized CSMA/CA is proposed for demand-matching spectrum allocation. A distributed game is formulated and no-regret learning is adopted to solve the game. Simulation results indicate increase on the number of satisfied CRs. In CCRNs, some nodes are selected as relays to assist the communication. A two-layer auction game is proposed with the first layer performing spectrum allocation and relay formation, and the second layer performing relay allocation. These two layers interact and jointly solve the resource allocation problem. Simulation results show that, compared to counterparts, both the network throughput and convergence time can be improved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".