Throughput reliability analysis of cloud-radio access networks
Bibliographic record
Abstract
This paper develops a stochastic geometry-based analytical approach for calculating the throughput reliability of a cloud-radio access network (C-RAN) comprising randomly distributed remote radio heads (RRHs) and randomly located users. A tunable distance-based RRH transmit power control mechanism along with cooperative joint transmissions by the RRHs is employed to achieve power savings and high throughput reliability. The analytical result for the throughput reliability serves as input to analysis of per user achievable average rate and C-RAN network-level performance metrics of spectral efficiency and energy efficiency. The analytical results are validated by Monte Carlo simulation results with good agreement, thus confirming the accuracy of the developed analytical approach. The key finding from the analysis is that by carefully tuning the RRH transmit power and cooperation parameter (cluster radius), it is possible to realize a threefold improvement in the energy efficiency along with 108% enhancement in the spectral efficiency of C-RANs. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".