Robust power allocation designs for cognitive radio networks with cooperative relays
Bibliographic record
Abstract
In this paper, we develop robust power allocation schemes for cognitive radio networks (CRNs) that can operate in multiple bands with cooperative relays considering uncertainty among the channels of secondary user (SU) network and among the channels of SU transmitters to primary user (PU) receivers. Our objective is to formulate the robust design optimization problems taking into account the interference threshold in the PU band specified by the regulatory guidelines. To optimally allocate power with channel uncertainty, two robust algorithms are developed: (i) the worst-case optimization, where the interference constraints are satisfied for all channels contained in some bounded uncertainty regions, and (ii) the probabilistically constrained optimization, where interference constraints are satisfied with certain probabilities. We show that the formulated problems are convex, which can be efficiently solved. Numerical results show the effectiveness of the proposed schemes and the implications of ignoring the uncertainties among different channels when designing power allocation schemes for CRNs.
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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 | 0.001 |
| 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".