Robust distributed two-way relay beamforming in cognitive radio networks
Bibliographic record
Abstract
In this paper, we present distributed beamformer designs for a cognitive radio network (CRN) consisting of a pair of cognitive (or secondary) transceiver nodes communicating with each other through a set of secondary non-regenerative two-way relays. The secondary network shares the spectrum with a licensed primary user (PU), and operates under a constraint on the maximum interference to the PU, in addition to its own resource and quality of service (QoS) constraints. We propose beamformer designs assuming that the available channel state information (CSI) is imperfect, which reflects realistic scenarios. The performance of proposed designs is robust to the CSI errors. Such robustness is critical in CRNs given the difficulty in acquiring perfect CSI due to loose cooperation between the PUs and the secondary users (SUs), and the need for strict enforcement of PU interference limit. We consider 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. We show that the proposed designs can be reformulated as convex optimization problems that can be solved efficiently. Through numerical simulations, we illustrate the improved performance of the proposed robust designs compared to non-robust 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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".