Distributed cognitive two-way relay beamformer designs under perfect and imperfect CSI
Bibliographic record
Abstract
In this paper, we present distributed two-way relay beamformer designs for a cognitive radio network (CRN) in which a pair of cognitive (or secondary) transceiver nodes communicate with each other assisted by a set of cognitive two-way relay nodes. The secondary nodes share the spectrum with a licensed primary user (PU) node, and each node is assumed to be equipped with a single transmit/receive antenna. The interference to the PU resulting from the transmission from the cognitive nodes is kept below a specified limit. First, we consider relay beamformer designs assuming the availability of perfect channel state information (CSI). For this case, a mean-square error (MSE)-constrained beamformer that minimizes the total relay transmit power, and an MSE-balancing beamformer with a constraint on the total relay transmit power are proposed. Next, we consider relay beamformer designs assuming that the available CSI is imperfect. For this case too, we consider the same problems as those in the case of perfect CSI, and propose beamformer designs that are robust to the errors in the CSI. We show that the proposed designs can be reformulated as convex optimization problems that can be solved efficiently. Through numerical simulations, we illustrate the performance of the proposed designs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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".