Joint Distributed Beamforming and Power Allocation in Underlay Cognitive Two-Way Relay Links Using Second-Order Channel Statistics
Bibliographic record
Abstract
Cognitive underlay amplify-and-forward (AF) two-way relay links require signal-to-interference-and-noise-ratio (SINR) balancing and maximizing strategies. And both secondary-to-primary and primary-to-secondary interference must be taken into consideration. To achieve these goals, we develop several joint distributed beamforming and power allocation algorithms. The development is based on the assumption of the availability of channel state information (CSI) of the relay-transceiver channels and the second-order-statistics (SOS) of all channels. For single-relay and multi-relay systems, a closed-form solution and an exhaustive-search optimal algorithm, respectively, are developed. This optimal algorithm, despite its high computational complexity, serves as a useful benchmark. Moreover, two low-complexity suboptimal algorithms are proposed, both of which compute sub-optimal power allocations and beamformers. A 10-dB SINR improvement by the optimal algorithm and similar gains by both proposed suboptimal algorithms are achievable.
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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 | 0.002 |
| 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".