Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks
Bibliographic record
Abstract
We investigate the trade-off between performance and energy efficiency in cooperative cognitive underlay systems. By cooperating, cognitive base stations mitigate the interference and serve their users more effectively at the cost of spending more power. With a flexible cooperation scheme, we thus jointly optimize the clustering and the beamforming to minimize the overall power consumption while satisfying the secondary users' quality of service and respecting the primary users' interference limits. We formulate this problem as a mixed-integer nonlinear program and decompose it into a master problem and a beamforming subproblem. Then, we derive an iterative algorithm based on the generalized Benders decomposition method to find an optimal solution. Moreover, we propose simple techniques to speed up its convergence. When the clusters are fixed and non-overlapping, we also propose a decentralized algorithm with limited signaling schemes using the alternating direction method of multipliers. In contrast to previous works, our distributed algorithm handles the total interference power constraints coupling the cognitive base stations. Through simulations, we analyze the effectiveness and convergence of the proposed algorithms and show the benefits of the cooperation in cognitive wireless networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".