A Novel Antenna Assignment Algorithm for Spectrum Underlay in Cognitive MIMO Networks
Bibliographic record
Abstract
We consider a point-to-multipoint cognitive network sharing the same frequency band with a primary network assuming a spectrum underlay model. We investigate the scenario where a cognitive base station equipped with multiple antennas attempts to serve secondary users through an antenna assignment scheme. We consider quality of service constraints for secondary users and an interference constraint for the primary receiver. Hence, the cognitive base station performs both antenna assignment and optimal power allocation for the selected secondary users. Due to the high computational complexity of this problem, we propose a heuristic algorithm that separates the two tasks and tries to maximize the number of served secondary users with respect to the system constraints. The antenna assignment phase is performed using an efficient selection criterion followed by an optimal power allocation as a second phase. We show that the proposed algorithm has a very low computational complexity compared to the brute force algorithm. Furthermore, simulation results show that the proposed heuristic algorithm is able to achieve performance very close to that of the optimal solution.
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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".