Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The exponential growth of cellular data traffic over the years imposes a hard challenge on the next cellular generations. The cloud radio access network is an emerging cellular architecture that is expected to face that challenge effectively. The main difference between the CRAN architecture and the conventional cellular architecture is that the baseband units (BBUs) are aggregated at a centralized BBU pool, hence, enabling statistical multiplexing gains. However, to acquire the several advantages induced from the CRAN architecture, efficient optimization algorithms should be used to enhance the network performance. In this article, we give an overview of cloud radio access networks, highlighting the advantages of its architecture over conventional cellular networks. Then we illustrate the performance metrics that should be considered for optimization in CRAN, and the important parameters to optimize. We review some of the recent research works in CRAN performance optimization. We highlight the current challenges faced in CRAN performance optimization and discuss open issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it