Fully distributed scheduling in cloud-RAN systems
Bibliographic record
Abstract
Cloud Radio Access Networks (C-RAN) promise to leverage cloud computing capabilities for enhancing the quality and coverage of next generation 5G networks. 5G networks shall witness an increasing density of users and access points, very small latencies, more bandwidth resources, and the use of virtualized hardware for baseband processing. Within such an environment, the problem of scheduling the network users across the radio resources might become a bottleneck of the system. In this paper, we study the problem of fully distributed scheduling in C-RAN systems. The idea is that each user's base band processing unit (BBU) tries to guess whether its user should be scheduled or not. First, we focus on the case of maximum throughput scheduling and Rayleigh channels, and provide closed-form expression for the expected effective channel and signal-to-noise ratio (SNR) in the distributed scenario. In order to deal with general channels and schedulers, we adopt the classification techniques from machine learning. We discover an interesting relationship between the fairness of the scheduler, and its ability to be distributed. In particular, schedulers which are more fair are also more prune to prediction errors in the distributed scenario. Finally, we provide simulation results that show that distributed scheduling can provide up to 90% of the performance of the centralized case.
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.
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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".