Performance improvements of interference alignment with multiuser diversity in cognitive radio networks
Bibliographic record
Abstract
Interference alignment (IA) is a recent breakthrough in interference management, and has been applied to cognitive radio. However, the signal-to-noise ratio (SNR) may decrease dramatically under some channel conditions, and this will reduce the quality of service (QoS) of primary users (PUs). In this paper, a novel IA scheme exploiting multiuser diversity is proposed for spectrum sharing. In the scheme, several secondary users (SUs) wish to access the licensed spectrum, and only the ones that maximally improve the QoS of PU will be selected to share the spectrum by forming an IA network with the PU. Thus the performance of PU can be significantly improved with the help of multiuser diversity brought by SUs. To further ensure the interests of SUs, the scheme is revised and a tradeoff is made between the PU and SUs. Simulation results are presented to show the effectiveness of the proposed schemes for spectrum sharing.
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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 | 0.000 | 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 | 0.000 |
| 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".