Dynamic Operations of Cloud Radio Access Networks (C-RAN) for Mobile Cloud Computing Systems
Bibliographic record
Abstract
In this paper, we jointly consider cloud radio access networks (C-RAN) and mobile cloud computing (MCC) in a holistic framework. In particular, we study how to dynamically operate C-RAN to enhance the end-to-end performance of MCC services in next-generation wireless networks. An intrinsic challenge in such a system is that channel state information (CSI) is outdated. With delayed CSI, only suboptimal C-RAN operations can be made if deterministic optimization techniques are applied directly. We formulate the topology configuration and rate-allocation problem with delayed CSI under a stochastic optimization framework. Such a framework maximizes MCC services' sum throughput with constraints on the response latency experienced by each MCC user. We propose an optimal policy for the stochastic optimization problem, which has the advantage of low computation cost. Offline and online algorithms are developed based on the optimal policy. Using simulation results, we show that, with the emergence of MCC and C-RAN technologies, the design and operation of future mobile wireless networks can be significantly affected by cloud computing, and the proposed scheme is capable of achieving substantial performance gains over existing schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".