Robust power control in cognitive radio networks with channel uncertainty
Bibliographic record
Abstract
In cognitive radio networks, channel information is desired by unlicensed secondary users (SUs) to perform effective power control so as to avoid undue interference to licensed primary users (PUs). However, in general, there is no regular information exchange between PUs and SUs, which implies that SUs are unable to obtain up-to-date channel information at the PU side. Besides, the small-scale fading, in addition to shadowing, brings great uncertainty in SUs' channel estimation. In this paper, we consider limited information exchange between SUs and PUs, and study the impact of channel uncertainty on SUs' throughput performance with power control. We model the uncertain channel gain to be a random variable following a state-dependent probability distribution function, and design a power control method that is robust against the channel uncertainty. We formulate the robust power control problem as a chance constrained robust optimization and solve it by an iterative algorithm. Numerical results show that the proposed power control can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".