QoS-aware Joint RRH activation and clustering in cloud-RANs
Bibliographic record
Abstract
Cloud Radio Access Networks (Cloud-RAN) promise to leverage cloud computing capabilities to enhance the quality and coverage of wireless networks. A dense network of remote radio heads (RRHs) ensures less attenuation at the receiver side. However, two drawbacks are associated with such dense network: the first is the high energy consumption associated with such a large number of RRHs; the second is the interference experienced by the receiver due to close proximity of the transmitters. To address these challenges, we study the problem of joint activation and clustering of RRHs. Since the problem is NP-hard, we provide a two-step algorithm that can find an efficient solution. The first step uses linear-programming relaxation to find a feasible solution. The second step is a greedy approach to improve the utility function through gradual activation-clustering of RRHs. Our simulation results demonstrate the benefit in the joint design of activation and clustering over existing activation only approaches.
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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".