Robust Beamforming for RIS Enhanced Transmissions in Cognitive Radio Networks
Bibliographic record
Abstract
We propose a robust beamforming (BF) scheme for reconfigurable intelligent surface (RIS) enhanced transmission to support heterogeneous services with diverse signal-to-interference-plus-noise ratio requirements in cognitive radio networks (CRNs). Here, the CRN coexisting with a primary network offers connection-centric services and content-aware services through space division multiple access and RIS-aided multicast technology, respectively. Using imperfect statistical channel state information, the RIS enhanced transmission scheme is formulated as a non-convex optimization problem with outage constraints. To address this intractable problem, we first use the cumulative distribution function of a standard normal distribution and Schur complement approaches to transform the non-convex outage constraints into solvable ones. Then, a robust BF algorithm integrating alternate optimization with semidefinite relaxation methods is proposed to obtain the active BF weight vectors at the cognitive base station and the phase shift matrix at the RIS. Our simulation results demonstrate the robustness of the proposed BF algorithm and the superiority of the RIS enhanced wireless transmission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".