Toward Green Deployment and Operation for C-RANs
Bibliographic record
Abstract
Introduction The boost in the number of mobile devices such as smart phones and tablets, together with the diverse applications enabled by mobile Internet, has triggered the exponential growth of mobile data traffic [1]. It is estimated that the next generation (5G) cellular networks will need to support a 1000-fold increase in traffic capacity [2]. With limited spectrum resources, it is challenging to accommodate the huge volume of traffic demand with conventional radio access network (RAN) architecture, in which the processing functionalities are packed into stand-alone base stations (BSs) and the cooperation between BSs is limited. In addition, 5G is expected to support massive connections including not only human-to-human connections but also machine-to-machine connections. Some demand a high data rate, while others have a low capacity requirement but require a real-time response and high reliability. As a result, the cellular network must be flexible enough to adapt to the various characteristics of different types of connections. Besides, under the influence of innovative applications from IT companies, the average revenue per user of network operators tends to increase slowly or even decrease in some cases, while expenditure increases rapidly [3]. Such trend imposes a great challenge to the sustainability of the cellular network. Therefore, it is crucial to renovate cellular network architectures to meet the requirements of 5G systems in terms of high efficiency, flexibility, and sustainability. One of the promising architecture evolution trends is integrating cloud computing technology into cellular networks, and accordingly cloud-RAN (C-RAN) [3] is proposed to move base band units (BBUs) of BSs to a centralized cloud computing platform, and only leaving remote radio heads (RRHs) in the front end. A similar idea is also proposed under the name wireless network cloud (WNC) [4]. In cloud-based cellular network architectures, software-defined BS functionalities are implemented on general purpose platforms (GPP) with virtualization technologies, making them virtual base stations (VBSs) [5-7]. Compared with conventional BSs, C-RAN is more flexible in terms of the implementation of new functionalities and the management of computational resource. The pooling of BBU processing brings statistical multiplexing gain not only for radio resources, via cooperative signal processing, but also for computational resources via BS function consolidation, thus potentially reducing operational cost [8]. In addition, centralized processing can also be combined with dynamic fronthaul switching to address the mobility and energy efficiency issues of small cells [9, 10].
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".