Combined antenna selection and beamforming in cross-layer design for cognitive networks
Bibliographic record
Abstract
We investigate the performance of an antenna selection algorithm applied with precoding to improve the throughput performance of cognitive multiple-input multiple-output (MIMO) radios. We assume cognitive MIMO radios coexist in the same frequency band with the primary users. In such event, for concurrent spectrum access, cognitive nodes use beamforming techniques to cancel the mutual information between cognitive and primary users. In that, cognitive nodes exploit the degrees of freedom offered by MIMO systems to beamform the transmitted signal in an attempt to cancel interference at primary users. In the cross-layer design, cognitive nodes maximize an objective function for the link layer throughput where precoding is applied at the physical layer on the transmitted spatial multiplexed signals. We present a closed form solution for the overall throughput in-terms of the physical layer parameters. Numerical results are presented to show the efficacy of the proposed scheme for different network settings.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".