Learning-Theoretic Multi-Channel Spectrum Sensing and Access in Full-Duplex Cognitive Radio Networks with Unknown Primary User Activities
Bibliographic record
Abstract
The majority of the opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. In this paper, we consider the problem of optimal opportunistic multi-channel spectrum sensing and access using full-duplex (FD) radios in the presence of uncertain primary user (PU) activity statistics. A joint learning and spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely affected. To address this problem, we formulate a robust optimization problem. Our analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. We analyze the impact of the sensing period on the PU collision probability and the SU throughput, and find the optimal sensing period via convex optimization. We show that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies also demonstrate that the resulting robust solution provides a good trade-off between optimizing the SU's throughput and protecting the PU.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".