Energy saving in C-RAN based on BBU switching scheme
Bibliographic record
Abstract
Cloud radio access network (C-RAN) is a novel mobile network architecture which can reduce energy consumption compare to the traditional RAN. There are lots of baseband units (BBU) in C-RAN. All BBUs are in active mode even when traffic load is light, this brings the problem of high energy consumption in BBU Pool. BBU switching scheme can switch off part of BBUs to save energy. It needs to consider BBUs' resource utilization rate but estimating BBUs' resource utilization rate is a challenging issue and lacks researches. In this paper, firstly we propose a novel scheme to estimate BBUs' resource utilization rate with the consideration of rate requirement of mobile user equipment (MUE), number of MUEs, bandwidth of Remote Radio Head (RRH) and the transmission power between RRH and MUE. Secondly, we provide a combine and remove (CnR) algorithm to decide BBU when to be switched off or to be switched on. It can remove overloaded BBU and underloaded BBU while combine them together and transfer them to normal BBU or sleeping BBU. The algorithm aims to maximize the number of sleeping BBUs without losing of Quality of Service (QoS). Numerical results indicate that the number of sleeping BBUs reaches maximum, BBUs' resource utilization rate keep in normal state and the energy consumption in the BBU Pool varies with the fluctuation of traffic load. In addition, our proposed scheme outperform the primary static scheme and schemes in relevant papers in terms of energy saving in the BBU Pool. The overall system using our scheme also consume less energy compare to the traditional RAN according to the provided energy consumption models.
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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".