Spectrum Band Selection in Delay-QoS Constrained Cognitive Radio Networks
Bibliographic record
Abstract
In this paper, a cognitive radio (CR) network with multiple spectrum bands available for secondary users (SUs) is considered. For the SU's active spectrum-band selection, two criteria are developed. One is to select the band with the highest secondary channel power gain, and the other is to select the band with the lowest interference channel power gain to primary users (PUs). With the quality-of-service (QoS) requirement concerning delay, the effective capacity (EC) behaviors over secondary links are investigated for both criteria under two spectrum-sharing constraints. To begin by presenting full benefits in these criteria, the constraint imposed on the secondary transmitter (ST) is the average interference limitation to PUs only. Furthermore, taking into account the ST's battery/energy budget, the ST is imposed by joint constraints on its average interference to PUs, as well as on its own average transmit power. For either constraint, we formulate the ST's optimal transmit power allocation to maximize the SU's EC with both band-selection criteria and, correspondingly, obtain the secondary's power allocation and maximum EC in closed forms. Numerical results demonstrated subsequently substantiate the validity of our derivations and provide a powerful tool for the spectrum-band selection in CR networks with multiple bands available.
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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 | 0.000 | 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".