Subcarrier, Bit and Power Allocation for Multiuser OFDM-Based Multi-Cell Cognitive Radio Systems
Bibliographic record
Abstract
We study the subcarrier, bit and power allocation problem for multiuser OFDM-based multi-cell cognitive radio (CR) systems in which one or more spectrum holes exist between multiple primary user (PU) frequency bands. The cognitive radio users (CRUs) are able to share any portion of the frequency band with other CRUs and the PUs as long as this does not interfere unduly with the PUs' transmissions. Both cochannel interference (CI) from other CRUs as well as mutual interference (MI) between the CRUs and the PUs are considered. The resource allocation problem is formulated as a multi-dimensional knapsack problem and a relatively simple, greedy max-min algorithm is proposed to solve it. Simulation results show that the max-min algorithm yields solutions which are close to (within 5% of) optimal. Sharing of the whole band can provide a substantial performance improvement over schemes which use guard bands to protect PU frequency bands and do not allow CRUs to use the PU bands.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".