Cognitive relaying and power allocation under channel state uncertainties
Bibliographic record
Abstract
In this paper, we present robust joint relay precoder designs and transceiver power allocations for a cognitive radio network under imperfect channel state information (CSI). The secondary (or cognitive) network consists of a pair of single-antenna transceiver nodes and a non-regenerative two-way relay with multiple antennas which aids the communication process between the transceiver pair. The secondary nodes share the spectrum with a licensed primary user (PU) while guaranteeing that the interference to the PU receiver is maintained below a specified threshold. We consider two robust designs: the first is based on the minimization of the total transmit power of the secondary relay node required to provide the minimum quality of service, measured in terms of mean-square error (MSE) of the transceiver nodes, and the second is based on the minimization of the sum-MSE of the transceiver nodes. The robust designs are based on worst-case optimization and take into account known parameters of the error in the CSI to render the performance immune to the presence of errors in the CSI. Though the original problem is non-convex, we show that the proposed designs can be reformulated as tractable convex optimization problems that can be solved efficiently. We illustrate the performance of the proposed designs through some selected numerical simulations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".