Joint multiuser admission control and downlink beamforming for green cloud-RANs Via semidefinite relaxation
Bibliographic record
Abstract
Cloud radio access network (Cloud-RAN) has great potentials to improve energy efficiency and increase capacity of wireless networks. In this paper, we study the green communication for Cloud-RANs. Instead of only focusing on the transmit power consumption, we minimize the network power consumption which includes not only transmit power consumption but also circuit power consumption. In addition, we take into account the scenario that some mobile users can not be served. A joint multiuser admission control and downlink beamforming scheme is proposed for Cloud-RANs to minimize the network power consumption. We first formulate the problem as a two-stage problem, and then adopt semidefinite relaxation (SDR) to transform it as a Mixed-Integer Semidefinite Program (MI-SDP) which is a convex programming when the integer variables are fixed. By using the continuous relaxation, a heuristic algorithm with low complexity is proposed to derive the suboptimal solution. The simulation results are presented to validate the effectiveness of our proposed algorithm compared with the existing work.
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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".