Bibliographic record
Abstract
The cognitive radio technology allows the design of dynamic spectrum sharing techniques where unlicensed secondary users can use frequency bands owned by license primary holders. Thus, this emerging technology is regarded as the ideal candidate that can enhance the efficiency of spectrum usage for the next generation of wireless communication systems. In this paper, we consider the problem of spectrum sharing and user scheduling in a cognitive radio MIMO system. A secondary network made up of a multi-antenna base station and several secondary receivers share the same frequency bands owned by primary users.We study the scenario where the primary receivers do not allow any interference from the cognitive BS which serves its users in the broadcast channel.Using graph theory, we propose a novel algorithm that finds a near optimal spectrum sharing with the objective of approaching the maximum achievable sum rate of the secondary network. The spectrum sharing problem is formulated as a new vertex coloring problem. We show that this problem is NP-hard and then we design an efficient greedy algorithm using one out of four proposed selection criteria to solve the problem. We also formulate the coloring problem as a binary integer programming problem in order to find the optimal coloring solution. Through computer simulations, it is shown that the proposed algorithm is able to achieve near-optimal performances with very low computational complexity.
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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".