Multiple-input multiple-output cross-layer antenna selection and beamforming for cognitive networks
Why this work is in the frame
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Bibliographic record
Abstract
Beamforming techniques can be used to suppress co-channel interference in radio devices. In a cognitive setting, beamforming can be beneficial as it can be applied to cancel interference among co-located primary users and cognitive users. In this study, the authors propose an antenna selection algorithm combined with zero-forcing beamforming to improve the throughput of cognitive multiple-input multiple-output (MIMO) radios. The algorithm consists of two phases. First, cognitive nodes apply antenna selection approach to achieve high transmission efficiency among communicating pairs. Cognitive nodes then exploit the spatial opportunities of MIMO systems and employ beamforming to cancel interference between cognitive and primary users. In that, the authors maximise an objective function for the system throughput where precoding is applied on the transmitted spatial multiplexed signals. Numerical results show the advantages offered by the proposed algorithm under different system scenarios.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it